Effects of sexual selection on non-sexual fitness components: a meta-analysis
Supplementary Material
Justin Cally, University of Melbourne
library(knitr)
library(pander)
library(compute.es)
library(metafor)
library(dplyr)
library(lme4)
library(forestplot)
library(ggplot2)
library(MuMIn)
library(glmulti)
library(cowplot)
library(ggrepel)
library(reshape2)Methods
Our aim was to investigate the effects of sexual selection on population fitness by conducting a meta-analysis on studies that measured fitness related outcomes after experimentally evolving a population under varying levels of opportunity for sexual selection. Here we describe the process of the literature search, data extraction, effect size calculation, formulation of multilevel models and assessing publication bias.
Literature Search (Extended methods from paper)
The literature search was conducted under the following conditions:
We searched ISI Web of Science and Scopus on 9th June 2017. Notably, these resulted in a different set of returns (PRISMA Figure).
Studies were restricted to those from peer-reviewed and in the English language.
We devised a search strategy that sought to find studies which manipulated the presence or strength of sexual selection using experimental evolution, and then measured some proxy of population fitness. As such the search terms were as follows:
ISI Web of Science
Topic (TS) = “Sexual Selection” OR Promisc* OR Monogam* OR Polygam* OR Polyandr* OR Polygyn* OR “Mate choice”
AND
Topic (TS) = Fitness OR “Population Fitness” OR Deleterious OR “Male Strength” OR Fecund* OR Viability OR Productiv* OR “Reproductive Success” OR “Reproductive Rate” OR Surviv* OR | “Development Rate” OR Extinct* OR “Competitive Success” OR Mortality OR Mass OR “Body Size” OR “Wing Size” OR Emergence OR Mating Rate OR “Mating Propensity” OR Adapt* OR “Novel | Environment” OR “Sexual Conflict” OR “Sexual Antagonis*”
AND
Topic (TS) = Generations OR “Experimental evolution” OR “mutation load”
AND
Research Area (SU) = “Evolutionary Biology”
Scopus
TITLE-ABS-KEY = “Sexual Selection” OR Promisc* OR Monogam* OR Polygam* OR Polyandr* OR Polygyn* OR “Mate choice”
AND
TITLE-ABS-KEY = Fitness OR “Population Fitness” OR Deleterious OR “Male Strength” OR Fecund* OR Viability OR Productiv* OR “Reproductive Success” OR “Reproductive Rate” OR Surviv* | OR “Development Rate” OR Extinct* OR “Competitive Success” OR Mortality OR Mass OR “Body Size” OR “Wing Size” OR Emergence OR Mating Rate OR “Mating Propensity” OR Adapt* OR | “Novel Environment” OR “Sexual Conflict” OR “Sexual Antagonis*”
AND
TITLE-ABS-KEY = Generations OR “Experimental evolution” OR “mutation load”
In addition to studies found from the literature search we also included three relevant studies that were identified during scoping but not picked up in the subsequent formal searches (Partridge 1980; Price et al. 2010; Savic Veselinovic et al. 2013) that were missed by the database searches (PRISMA Figure).
- After removing duplicates, we read the titles and abstracts of the remaining 1015 papers, and removed papers that were not relevant (typically because they were not an empirical study using experimental evolution). This left 130 papers, for which we read the full text and applied the following selection criteria:
- (1: Study Design) The study was an experimental evolution study lasting >1 generation
- (1: Population) a) The study was conducted using an animal species that was b) diecious
- (1: Intervention and Control) The study experimentally manipulated the strength of sexual selection (e.g. via enforced monogamy or an altered sex ratio)
- (1: Outcomes) The study measured a trait that we judged to be a potential correlate of population fitness.
This latter criterion is likely to be contentious, because there is rarely enough data justify the assumption that a particular trait is (or is not) correlated with population fitness. We therefore relied on our best judgement when deciding which studies to exclude (see Table XXX). The inclusion/exlusion critera as applied to each study are detailed in an accompaning spreadsheet.
Eligibility.criteria <- read.csv('Eligibility Workbook(22.02).csv', fileEncoding="UTF-8")
Eligibility.criteria %>% pander(split.cell = 20, split.table = 250, style = "grid")
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Authors | Year | Title | Study.Design | Population | Intervention.and.Control | Outcomes | Included | Exclusion.Reason | Notes |
+======================+======+=======================+==============+============+==========================+==========+==========+==================+======================+
| Aguirre, J. D. and | 2012 | Does Genetic | No | | | | No | 1 | |
| D. J. Marshall | | Diversity Reduce | | | | | | | |
| | | Sibling Competition? | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Ahuja, A. and R. S. | 2008 | Variation and | No | | | | No | 1 | |
| Singh | | evolution of male | | | | | | | |
| | | sex combs in | | | | | | | |
| | | Drosophila: Nature | | | | | | | |
| | | of selection | | | | | | | |
| | | response and | | | | | | | |
| | | theories of genetic | | | | | | | |
| | | variation for sexual | | | | | | | |
| | | traits | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Almbro, M. and L. W. | 2014 | Sexual Selection Can | Yes | Yes | Yes | Yes | Yes | | Male strength is |
| Simmons | | Remove an | | | | | | | important in |
| | | Experimentally | | | | | | | competition |
| | | Induced Mutation | | | | | | | |
| | | Load | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Amitin, E. G. and S. | 2007 | Influence of | Yes | Yes | No | | No | 3 | |
| Pitnick | | developmental | | | | | | | |
| | | environment on male- | | | | | | | |
| | | and female-mediated | | | | | | | |
| | | sperm precedence in | | | | | | | |
| | | Drosophila | | | | | | | |
| | | melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Antolin, M. F., P. | 2003 | Population | No | | | | No | 1 | |
| J. Ode, G. E. | | structure, mating | | | | | | | |
| Heimpel, R. B. | | system, and | | | | | | | |
| O'Hara and M. R. | | sex-determining | | | | | | | |
| Strand | | allele diversity of | | | | | | | |
| | | the parasitoid wasp | | | | | | | |
| | | Habrobracon hebetor | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Arbuthnott, D., E. | 2014 | The ecology of | Yes | Yes | No | | No | 3 | |
| M. Dutton, A. F. | | sexual conflict: | | | | | | | |
| Agrawal and H. D. | | ecologically | | | | | | | |
| Rundle | | dependent parallel | | | | | | | |
| | | evolution of male | | | | | | | |
| | | harm and female | | | | | | | |
| | | resistance in | | | | | | | |
| | | Drosophila | | | | | | | |
| | | melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Arbuthnott, D. and | 2012 | Sexual Selection Is | Yes | Yes | Yes | Yes | Yes | | Natural selection |
| H. D. Rundle | | Ineffectual or | | | | | | | acted against tested |
| | | Inhibits the Purging | | | | | | | alleles, thus |
| | | of Deleterious | | | | | | | indicate fitness |
| | | Mutations in | | | | | | | aspect |
| | | Drosophila | | | | | | | |
| | | Melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Arbuthnott, D. and | 2014 | Misalignment of | Yes | Yes | No | | No | 3 | |
| H. D. Rundle | | natural and sexual | | | | | | | |
| | | selection among | | | | | | | |
| | | divergently adapted | | | | | | | |
| | | Drosophila | | | | | | | |
| | | melanogaster | | | | | | | |
| | | populations | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Archer, C. R., E. | 2015 | Sex-specific effects | Yes | Yes | Yes | Yes | Yes | | Natural selection |
| Duffy, D. J. Hosken, | | of natural and | | | | | | | was measured |
| M. Mokkonen, K. | | sexual selection on | | | | | | | simultanous and thus |
| Okada, K. Oku, M. D. | | the evolution of | | | | | | | provides measurement |
| Sharma and J. Hunt | | life span and ageing | | | | | | | of fitness |
| | | in Drosophila | | | | | | | |
| | | simulans | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Artieri, C. G., W. | 2008 | Sexual selection and | No | | | | No | 1 | |
| Haerty, B. P. Gupta | | maintenance of sex: | | | | | | | |
| and R. S. Singh | | Evidence from | | | | | | | |
| | | comparisons of rates | | | | | | | |
| | | of genomic | | | | | | | |
| | | accumulation of | | | | | | | |
| | | mutations and | | | | | | | |
| | | divergence of | | | | | | | |
| | | sex-related genes in | | | | | | | |
| | | sexual and | | | | | | | |
| | | hermaphroditic | | | | | | | |
| | | species of | | | | | | | |
| | | Caenorhabditis | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Bacigalupe, L. D., | 2007 | Sexual conflict does | Yes | Yes | Yes | No | No | 4 | Viability and |
| H. S. Crudgington, | | not drive | | | | | | | sterility were |
| F. Hunter, A. J. | | reproductive | | | | | | | measured + Mating |
| Moore and R. R. | | isolation in | | | | | | | Speed, However these |
| Snook | | experimental | | | | | | | were in crosses, |
| | | populations of | | | | | | | refer to 2008 study |
| | | Drosophila | | | | | | | for beater outcomes |
| | | pseudoobscura | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Bacigalupe, L. D., | 2008 | Sexual selection and | Yes | Yes | Yes | Yes | No | Not Suitable | Mating speed cited |
| H. S. Crudgington, | | interacting | | | | | | | as a measure of |
| J. Slate, A. J. | | phenotypes in | | | | | | | fitness. Uses both |
| Moore and R. R. | | experimental | | | | | | | male and female. |
| Snook | | evolution: A study | | | | | | | Doesn't really work. |
| | | of Drosophila | | | | | | | |
| | | pseudoobscura mating | | | | | | | |
| | | behavior | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Barbosa, M., S. R. | 2012 | Fitness consequences | No | | | | No | 1 | Measures multiple |
| Connolly, M. Hisano, | | of female multiple | | | | | | | mating not choice |
| M. Dornelas and A. | | mating: A direct | | | | | | | |
| E. Magurran | | test of indirect | | | | | | | |
| | | benefits | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Bernasconi, G. and | 2001 | Female polyandry | Yes | Yes | Yes | Unsure | Yes | | Polyandry was done |
| L. Keller | | affects their sons' | | | | | | | sequentially with |
| | | reproductive success | | | | | | | postcop mate choice, |
| | | in the red flour | | | | | | | however the |
| | | beetle Tribolium | | | | | | | experimental |
| | | castaneum | | | | | | | approach and |
| | | | | | | | | | measurement of |
| | | | | | | | | | outcomes meant that |
| | | | | | | | | | extracting SS+ vs |
| | | | | | | | | | SS- was not possible |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Bielak, A. P., A. M. | 2014 | Selection for | No | | | | No | 1 | Artificial selection |
| Skrzynecka, K. Miler | | alternative male | | | | | | | was conducted |
| and J. Radwan | | reproductive tactics | | | | | | | |
| | | alters intralocus | | | | | | | |
| | | sexual conflict | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Blows, M. W. | 2002 | Interaction between | Yes | Yes | Yes | No | No | 4 | Hybrid Drosophilia |
| | | natural and sexual | | | | | | | used, indirect |
| | | selection during the | | | | | | | fitness was measured |
| | | evolution of mate | | | | | | | (mate recognition |
| | | recognition | | | | | | | system) |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Brommer, J. E., C. | 2012 | Interactions between | Yes | Yes | Yes | Yes | Yes | | Multiple males but |
| Fricke, D. A. Edward | | Genotype and Sexual | | | | | | | only one at a time ( |
| and T. Chapman | | Conflict Environment | | | | | | | could be post |
| | | Influence | | | | | | | copulatory SS) |
| | | Transgenerational | | | | | | | |
| | | Fitness in | | | | | | | |
| | | Drosophila | | | | | | | |
| | | Melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Castillo, D. M., M. | 2015 | Experimental | Yes | Yes | No | | No | 3 | No SS lines |
| K. Burger, C. M. | | evolution: | | | | | | | |
| Lively and L. F. | | Assortative mating | | | | | | | |
| Delph | | and sexual | | | | | | | |
| | | selection, | | | | | | | |
| | | independent of local | | | | | | | |
| | | adaptation, lead to | | | | | | | |
| | | reproductive | | | | | | | |
| | | isolation in the | | | | | | | |
| | | nematode | | | | | | | |
| | | Caenorhabditis | | | | | | | |
| | | remanei | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Cayetano, L., A. A. | 2011 | Evolution of Male | Yes | Yes | Yes | No | No | 4 | Conflict I |
| Maklakov, R. C. | | and Female Genitalia | | | | | | | burdensome and |
| Brooks and R. | | Following Release | | | | | | | defensive / |
| Bonduriansky | | from Sexual | | | | | | | offensive traits |
| | | Selection | | | | | | | have fitness costs |
| | | | | | | | | | and benefits: |
| | | | | | | | | | Removing as too |
| | | | | | | | | | difficult to see |
| | | | | | | | | | clear fitness of |
| | | | | | | | | | measurements |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Chandler, C. H., C. | 2013 | Runaway Sexual | Yes | No | | | No | 2a | Digital organisms |
| Ofria and I. Dworkin | | Selection Leads to | | | | | | | used |
| | | Good Genes | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Chenoweth, S. F., N. | 2015 | Genomic Evidence | Yes | Yes | Yes | No | No | 4 | Alongside direct |
| C. Appleton, S. L. | | that Sexual | | | | | | | fitness, SNPs also |
| Allen and H. D. | | Selection Impedes | | | | | | | used. This paper |
| Rundle | | Adaptation to a | | | | | | | reports SNPs while |
| | | Novel Environment | | | | | | | Rundle (2006) |
| | | | | | | | | | reports fitness |
| | | | | | | | | | measures. Thus data |
| | | | | | | | | | is extracted from |
| | | | | | | | | | that paper, not this |
| | | | | | | | | | one |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Chenoweth, S. F., D. | 2007 | Male choice | No | | | | No | 1 | Behavioural mate |
| Petfield, P. Doughty | | generates | | | | | | | choice experiment |
| and M. W. Blows | | stabilizing sexual | | | | | | | |
| | | selection on a | | | | | | | |
| | | female fecundity | | | | | | | |
| | | correlate | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Chenoweth, S. F., H. | 2008 | Genetic constraints | Yes | Yes | Yes | No | No | 4 | Natural selection |
| D. Rundle and M. W. | | and the evolution of | | | | | | | was also measured |
| Blows | | display trait sexual | | | | | | | and CHCs provide an |
| | | dimorphism by | | | | | | | indirect fitness |
| | | natural and sexual | | | | | | | aspect |
| | | selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Chenoweth, S. F., H. | 2010 | Experimental | Yes | Yes | Yes | No | No | 4 | CHCs may provide |
| D. Rundle and M. W. | | evidence for the | | | | | | | indirect fitness |
| Blows | | evolution of | | | | | | | aspect |
| | | indirect genetic | | | | | | | |
| | | effects: changes in | | | | | | | |
| | | the interaction | | | | | | | |
| | | effect coefficient, | | | | | | | |
| | | psi (_), due to | | | | | | | |
| | | sexual selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Crudgington, H. S., | 2005 | Experimental removal | Yes | Yes | Yes | Yes | Yes | | |
| A. P. Beckerman, L. | | and elevation of | | | | | | | |
| Brustle, K. Green | | sexual selection: | | | | | | | |
| and R. R. Snook | | Does sexual | | | | | | | |
| | | selection generate | | | | | | | |
| | | manipulative males | | | | | | | |
| | | and resistant | | | | | | | |
| | | females? | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Crudgington, H. S., | 2009 | Experimental | Yes | Yes | Yes | Yes | Yes | | Direct and indirect |
| S. Fellows, N. S. | | Manipulation of | | | | | | | outcomes |
| Badcock and R. R. | | Sexual Selection | | | | | | | |
| Snook | | Promotes Greater | | | | | | | |
| | | Male Mating Capacity | | | | | | | |
| | | but Does Not Alter | | | | | | | |
| | | Sperm Investment | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Crudgington, H. S., | 2010 | Increased | Yes | Yes | Yes | Yes | Yes | | |
| S. Fellows and R. R. | | opportunity for | | | | | | | |
| Snook | | sexual conflict | | | | | | | |
| | | promotes harmful | | | | | | | |
| | | males with elevated | | | | | | | |
| | | courtship | | | | | | | |
| | | frequencies | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Debelle, A., M. G. | 2016 | Sexual selection and | Yes | Yes | Yes | Yes | Yes | | |
| Ritchie and R. R. | | assortative mating: | | | | | | | |
| Snook | | an experimental test | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Demont, M., V. M. | 2014 | Experimental Removal | Yes | Yes | Yes | Yes | Yes | | |
| Grazer, L. | | of Sexual Selection | | | | | | | |
| Michalczyk, A. L. | | Reveals Adaptations | | | | | | | |
| Millard, S. H. | | to Polyandry in Both | | | | | | | |
| Sbilordo, B. C. | | Sexes | | | | | | | |
| Emerson, M. J. G. | | | | | | | | | |
| Gage and O. Y. | | | | | | | | | |
| Martin | | | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Edward, D. A., C. | 2010 | Adaptations to | Yes | Yes | Yes | Yes | Yes | | |
| Fricke and T. | | sexual selection and | | | | | | | |
| Chapman | | sexual conflict: | | | | | | | |
| | | insights from | | | | | | | |
| | | experimental | | | | | | | |
| | | evolution and | | | | | | | |
| | | artificial selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Fava, G. | 1975 | Studies on the | Yes | Yes | No | | No | 3 | |
| | | selective agents | | | | | | | |
| | | operating in | | | | | | | |
| | | experimental | | | | | | | |
| | | populations of Tisbe | | | | | | | |
| | | clodiensis | | | | | | | |
| | | (Copepoda, | | | | | | | |
| | | Harpacticoida) | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Firman, R. C. | 2011 | Polyandrous females | Yes | Yes | Yes | Yes | Yes | | It looks like post |
| | | benefit by producing | | | | | | | copulatory selection |
| | | sons that achieve | | | | | | | was enabled here |
| | | high reproductive | | | | | | | |
| | | success in a | | | | | | | |
| | | competitive | | | | | | | |
| | | environment | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Firman, R. C. | 2014 | Female social | Yes | Yes | Yes | Yes | Yes | | The outcome measured |
| | | preference for males | | | | | | | was female |
| | | that have evolved | | | | | | | preference and male |
| | | via monogamy: | | | | | | | scent marking rate. |
| | | evidence of a | | | | | | | May have a role in |
| | | trade-off between | | | | | | | fitness but not |
| | | pre- and | | | | | | | explicitly stated |
| | | post-copulatory | | | | | | | |
| | | sexually selected | | | | | | | |
| | | traits? | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Firman, R. C., L. Y. | 2011 | Sperm competition | Yes | Yes | Yes | Yes | Yes | | Sperm quality was |
| Cheam and L. W. | | does not influence | | | | | | | measured |
| Simmons | | sperm hook | | | | | | | |
| | | morphology in | | | | | | | |
| | | selection lines of | | | | | | | |
| | | house mice | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Firman, R. C., F. | 2015 | Evolutionary change | Yes | Yes | Yes | Yes | Yes | | Amount of sperm |
| Garcia-Gonzalez, E. | | in testes tissue | | | | | | | producing tissue was |
| Thyer, S. Wheeler, | | composition among | | | | | | | measured as it |
| Z. Yamin, M. Yuan | | experimental | | | | | | | provides an |
| and L. W. Simmons | | populations of house | | | | | | | advantage in sperm |
| | | mice | | | | | | | competition |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Firman, R. C., M. | 2014 | The Coevolution of | Yes | Yes | Yes | Yes | Yes | | ova defensivenenss |
| Gomendio, E. R. S. | | Ova Defensiveness | | | | | | | can bias |
| Roldan and L. W. | | with Sperm | | | | | | | fertilization to a |
| Simmons | | Competitiveness in | | | | | | | more specific type |
| | | House Mice | | | | | | | of sperm and thus be |
| | | | | | | | | | a fitness adavantage |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Firman, R. C. and L. | 2010 | Experimental | Yes | Yes | Yes | Yes | Yes | | Polygamous lines |
| W. Simmons | | Evolution of Sperm | | | | | | | have only |
| | | Quality Via | | | | | | | post-copulatory |
| | | Postcopulatory | | | | | | | selection |
| | | Sexual Selection in | | | | | | | |
| | | House Mice | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Firman, R. C. and L. | 2011 | Experimental | Yes | Yes | Yes | Yes | Yes | | Sperm competition is |
| W. Simmons | | evolution of sperm | | | | | | | a fitness advantage |
| | | competitiveness in a | | | | | | | |
| | | mammal | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Firman, R. C. and L. | 2012 | Male house mice | Yes | Yes | Yes | Yes | Yes | | Post cop SS |
| W. Simmons | | evolving with | | | | | | | |
| | | post-copulatory | | | | | | | |
| | | sexual selection | | | | | | | |
| | | sire embryos with | | | | | | | |
| | | increased viability | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Fricke, C., C. | 2010 | Natural selection | Yes | Yes | Yes | No | No | 4 | Could not use the |
| Andersson and G. | | hampers divergence | | | | | | | broad outcome of |
| Arnqvist | | of reproductive | | | | | | | reproductive |
| | | traits in a seed | | | | | | | characteristics as |
| | | beetle | | | | | | | it is not |
| | | | | | | | | | directional |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Fricke, C. and G. | 2007 | Rapid adaptation to | Yes | Yes | Yes | Yes | Yes | | Also post cop |
| Arnqvist | | a novel host in a | | | | | | | |
| | | seed beetle | | | | | | | |
| | | (Callosobruchus | | | | | | | |
| | | maculatus): The role | | | | | | | |
| | | of sexual selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Fritzsche, K., I. | 2016 | Sex Ratio Bias Leads | Yes | Yes | Yes | Yes | Yes | | Male bias and female |
| Booksmythe and G. | | to the Evolution of | | | | | | | bias setups without |
| Arnqvist | | Sex Role Reversal in | | | | | | | monogamus/lack of SS |
| | | Honey Locust Beetles | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Fritzsche, K., N. | 2014 | Female, but not | Yes | Yes | Yes | Yes | Yes | | Male bias and female |
| Timmermeyer, M. | | male, nematodes | | | | | | | bias setups without |
| Wolter and N. K. | | evolve under | | | | | | | monogamus/lack of SS |
| Michiels | | experimental sexual | | | | | | | |
| | | coevolution | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Garcia-Gonzalez, F., | 2015 | Mating portfolios: | Yes | Yes | Yes | Yes | No | Not Suitable | Experiments run |
| Y. Yasui and J. P. | | bet-hedging, sexual | | | | | | | alongside |
| Evans | | selection and female | | | | | | | bet-hedging, perhaps |
| | | multiple mating | | | | | | | confounding, need to |
| | | | | | | | | | look at data |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Gay, L., P. E. Eady, | 2009 | Does reproductive | Yes | Yes | No | | No | 3 | Generations of |
| R. Vasudev, D. J. | | isolation evolve | | | | | | | monoandry were |
| Hosken and T. | | faster in larger | | | | | | | replaced by |
| Tregenza | | populations via | | | | | | | polyandry (not done |
| | | sexually | | | | | | | simultaneously ), |
| | | antagonistic | | | | | | | Not sure whether the |
| | | coevolution? | | | | | | | monogamous lines |
| | | | | | | | | | were maintained. |
| | | | | | | | | | This experiment was |
| | | | | | | | | | focussed on |
| | | | | | | | | | reproductive |
| | | | | | | | | | isolation anyway |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Gay, L., D. J. | 2011 | The Evolution of | Yes | Yes | No | | No | 3 | Generations of |
| Hosken, P. Eady, R. | | Harm-Effect of | | | | | | | monoandry were |
| Vasudev and T. | | Sexual Conflicts and | | | | | | | replaced by |
| Tregenza | | Population Size | | | | | | | polyandry (not done |
| | | | | | | | | | simultaneously ), |
| | | | | | | | | | Not sure whether the |
| | | | | | | | | | monogamous lines |
| | | | | | | | | | were maintained. |
| | | | | | | | | | Also, did not |
| | | | | | | | | | directly look at SS+ |
| | | | | | | | | | vs SS- |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Gay, L., D. J. | 2009 | Sperm competition | Yes | Yes | Yes | Yes | Yes | | Appears to be direct |
| Hosken, R. Vasudev, | | and maternal effects | | | | | | | comparison bw mono |
| T. Tregenza and P. | | differentially | | | | | | | and poly |
| E. Eady | | influence testis and | | | | | | | |
| | | sperm size in | | | | | | | |
| | | Callosobruchus | | | | | | | |
| | | maculatus | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Grazer, V. M., M. | 2014 | Environmental | Yes | Yes | Yes | Yes | Yes | | Direct Measures of |
| Demont, L. | | quality alters | | | | | | | fitness in |
| Michalczyk, M. J. G. | | female costs and | | | | | | | environments that |
| Gage and O. Y. | | benefits of evolving | | | | | | | had standard and |
| Martin | | under enforced | | | | | | | sub-standard food |
| | | monogamy | | | | | | | quality |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Grieshop, K., J. | 2016 | Strong sexual | Yes | Yes | No | | No | 3 | Different mating |
| Stangberg, I. | | selection in males | | | | | | | systems/ opportunity |
| Martinossi-Allibert, | | against a mutation | | | | | | | for SS were not |
| G. Arnqvist and D. | | load that reduces | | | | | | | imposed |
| Berger | | offspring production | | | | | | | |
| | | in seed beetles | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hall, M. D., L. F. | 2009 | Diet-dependent | Yes | Yes | No | | No | 3 | Different mating |
| Bussiere and R. | | female evolution | | | | | | | systems/ opportunity |
| Brooks | | influences male | | | | | | | for SS were not |
| | | lifespan in a | | | | | | | imposed |
| | | nuptial feeding | | | | | | | |
| | | insect | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hangartner, S., L. | 2015 | Experimental removal | Yes | Yes | Yes | Yes | Yes | | |
| Michalczyk, M. J. G. | | of sexual selection | | | | | | | |
| Gage and O. Y. | | leads to decreased | | | | | | | |
| Martin | | investment in an | | | | | | | |
| | | immune component in | | | | | | | |
| | | female Tribolium | | | | | | | |
| | | castaneum | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hangartner, S., S. | 2013 | Are there genetic | Yes | Yes | Yes | Yes | Yes | | Different levels of |
| H. Sbilordo, L. | | trade-offs between | | | | | | | SS, but none with |
| Michalczyk, M. J. G. | | immune and | | | | | | | enforced monogamy |
| Gage and O. Y. | | reproductive | | | | | | | (no choice) |
| Martin | | investments in | | | | | | | |
| | | Tribolium castaneum? | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hicks, S. K., K. L. | 2004 | Variable costs of | Yes | Yes | No | | No | 3 | Study on |
| Hagenbuch and L. M. | | mating, longevity, | | | | | | | environmental |
| Meffert | | and starvation | | | | | | | conditions not SS |
| | | resistance in Musca | | | | | | | treatment |
| | | domestica (Diptera: | | | | | | | |
| | | Muscidae) | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Holland, B. | 2002 | Sexual selection | Yes | Yes | Yes | Yes | Yes | | Also looks at |
| | | fails to promote | | | | | | | thermal stress |
| | | adaptation to a new | | | | | | | |
| | | environment | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Holland, B. and W. | 1999 | Experimental removal | Yes | Yes | Yes | Yes | Yes | | |
| R. Rice | | of sexual selection | | | | | | | |
| | | reverses intersexual | | | | | | | |
| | | antagonistic | | | | | | | |
| | | coevolution and | | | | | | | |
| | | removes a | | | | | | | |
| | | reproductive load | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hollis, B., J. L. | 2009 | Sexual Selection | Yes | Yes | Yes | Yes | Yes | | looked at the |
| Fierst and D. Houle | | Accelerates the | | | | | | | purging of a |
| | | Elimination of a | | | | | | | deleterious allele |
| | | Deleterious Mutant | | | | | | | |
| | | in Drosophila | | | | | | | |
| | | Melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hollis, B. and D. | 2011 | Populations with | Yes | Yes | Yes | Yes | Yes | | Mutagenesis took |
| Houle | | elevated mutation | | | | | | | place and direct |
| | | load do not benefit | | | | | | | fitness measurements |
| | | from the operation | | | | | | | were made |
| | | of sexual selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hollis, B., D. Houle | 2016 | Evolution of reduced | Yes | Yes | Yes | No | No | 4 | Seminal fluid |
| and T. J. Kawecki | | post-copulatory | | | | | | | proteins have a |
| | | molecular | | | | | | | fitness advantage in |
| | | interactions in | | | | | | | a polygamous |
| | | Drosophila | | | | | | | setting, thus is |
| | | populations lacking | | | | | | | favoured. Feels like |
| | | sperm competition | | | | | | | a bit of a circular |
| | | | | | | | | | argument |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hollis, B., D. | 2014 | Evolution under | Yes | Yes | Yes | No | No | 4 | Sex biased gene |
| Houle, Z. Yan, T. J. | | monogamy feminizes | | | | | | | expression was |
| Kawecki and L. | | gene expression in | | | | | | | measured, showing |
| Keller | | Drosophila | | | | | | | sexual antagonism |
| | | melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hollis, B. and T. J. | 2014 | Male cognitive | Yes | Yes | Yes | Yes | Yes | | Cognitive ability |
| Kawecki | | performance declines | | | | | | | measured in both |
| | | in the absence of | | | | | | | male and female |
| | | sexual selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hollis, B., L. | 2017 | Sexual selection | Yes | Yes | Yes | Yes | Yes | | Development and |
| Keller and T. J. | | shapes development | | | | | | | fitness measured |
| Kawecki | | and maturation rates | | | | | | | |
| | | in Drosophila | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hosken, D. J., O. Y. | 2009 | Sexual conflict and | Yes | Yes | Yes | No | No | 4 | Reproductive |
| Martin, S. Wigby, T. | | reproductive | | | | | | | isolation measured |
| Chapman and D. J. | | isolation in flies | | | | | | | without fitness |
| Hodgson | | | | | | | | | components |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| House, C. M., Z. | 2013 | Sexual and Natural | Yes | Yes | Yes | No | No | 4 | Indirect fitness |
| Lewis, D. J. | | Selection Both | | | | | | | component of male |
| Hodgson, N. Wedell, | | Influence Male | | | | | | | genitalia with |
| M. D. Sharma, J. | | Genital Evolution | | | | | | | citation |
| Hunt and D. J. | | | | | | | | | |
| Hosken | | | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Hunt, J., R. R. | 2012 | Sexual selection and | Yes | Yes | Yes | No | No | 4 | Body size measured |
| Snook, C. Mitchell, | | experimental | | | | | | | as well as CHC, like |
| H. S. Crudgington | | evolution of | | | | | | | other studies may |
| and A. J. Moore | | chemical signals in | | | | | | | confer fitness |
| | | Drosophila | | | | | | | advantage |
| | | pseudoobscura | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Immonen, E., R. R. | 2014 | Mating system | Yes | Yes | Yes | Yes | Yes | | While transcriptome |
| Snook and M. G. | | variation drives | | | | | | | outcomes not |
| Ritchie | | rapid evolution of | | | | | | | exclusively |
| | | the female | | | | | | | measuring fitness |
| | | transcriptome in | | | | | | | they also measures |
| | | Drosophila | | | | | | | aspects of fecundity |
| | | pseudoobscura | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Innocenti, P., I. | 2014 | Female responses to | Yes | Yes | Yes | Yes | Yes | | To some extent the |
| Flis and E. H. | | experimental removal | | | | | | | nature of SS |
| Morrow | | of sexual selection | | | | | | | treatment is |
| | | components in | | | | | | | unclear. Gene |
| | | Drosophila | | | | | | | expression and |
| | | melanogaster | | | | | | | fecundity are |
| | | | | | | | | | measured |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Jacomb, F., J. Marsh | 2016 | Sexual selection | Yes | Yes | Yes | Yes | Yes | | Pesticide Resistance |
| and L. Holman | | expedites the | | | | | | | as an environmental |
| | | evolution of | | | | | | | condition that needs |
| | | pesticide resistance | | | | | | | to be adapted to |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Janicke, T., P. | 2016 | Experimentally | Yes | No | | | No | 2b | Hermaphroditic |
| Sandner, S. A. Ramm, | | evolved and | | | | | | | |
| D. B. Vizoso and L. | | phenotypically | | | | | | | |
| Schaerer | | plastic responses to | | | | | | | |
| | | enforced monogamy in | | | | | | | |
| | | a hermaphroditic | | | | | | | |
| | | flatworm | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Jarzebowska, M. and | 2010 | Sexual Selection | Yes | Yes | Yes | Yes | Yes | | Direct fitness |
| J. Radwan | | Counteracts | | | | | | | measurements over |
| | | Extinction of Small | | | | | | | several generations |
| | | Populations of the | | | | | | | |
| | | Bulb Mites | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Klemme, I. and R. C. | 2013 | Male house mice that | Yes | Yes | Yes | Yes | Yes | | Paternity Success |
| Firman | | have evolved with | | | | | | | measured |
| | | sperm competition | | | | | | | |
| | | have increased | | | | | | | |
| | | mating duration and | | | | | | | |
| | | paternity success | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Long, T. A. F., A. | 2012 | The Effect of Sexual | Yes | Yes | No | | No | 3 | No enforced SS |
| F. Agrawal and L. | | Selection on | | | | | | | regimes |
| Rowe | | Offspring Fitness | | | | | | | |
| | | Depends on the | | | | | | | |
| | | Nature of Genetic | | | | | | | |
| | | Variation | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Lumley, A. J., L. | 2015 | Sexual selection | Yes | Yes | Yes | Yes | Yes | | Reproductive fitness |
| Michalczyk, J. J. N. | | protects against | | | | | | | and time to |
| Kitson, L. G. | | extinction | | | | | | | extinction measured |
| Spurgin, C. A. | | | | | | | | | |
| Morrison, J. L. | | | | | | | | | |
| Godwin, M. E. | | | | | | | | | |
| Dickinson, O. Y. | | | | | | | | | |
| Martin, B. C. | | | | | | | | | |
| Emerson, T. Chapman | | | | | | | | | |
| and M. J. G. Gage | | | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| MacLellan, K., L. | 2012 | Dietary stress does | No | | | | No | 1 | Selection based |
| Kwan, M. C. Whitlock | | not strengthen | | | | | | | experiment rather |
| and H. D. Rundle | | selection against | | | | | | | than EE |
| | | single deleterious | | | | | | | |
| | | mutations in | | | | | | | |
| | | Drosophila | | | | | | | |
| | | melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| MacLellan, K., M. C. | 2009 | Sexual selection | No | | | | No | 1 | Selection based |
| Whitlock and H. D. | | against deleterious | | | | | | | experiment rather |
| Rundle | | mutations via | | | | | | | than EE |
| | | variable male search | | | | | | | |
| | | success | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Maklakov, A. A., R. | 2009 | Sex Differences, | Yes | Yes | Yes | Yes | Yes | | Life History traits |
| Bonduriansky and R. | | Sexual Selection, | | | | | | | were measured |
| C. Brooks | | and Ageing: An | | | | | | | |
| | | Experimental | | | | | | | |
| | | Evolution Approach | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Maklakov, A. A. and | 2009 | Sexual selection did | Yes | Yes | Yes | No | No | 4 | Life History traits |
| C. Fricke | | not contribute to | | | | | | | were measured |
| | | the evolution of | | | | | | | |
| | | male lifespan under | | | | | | | |
| | | curtailed age at | | | | | | | |
| | | reproduction in a | | | | | | | |
| | | seed beetle | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Maklakov, A. A., C. | 2007 | Sexual selection | Yes | Yes | Yes | No | No | 4 | Life History traits |
| Fricke and G. | | affects lifespan and | | | | | | | were measured |
| Arnqvist | | aging in the seed | | | | | | | |
| | | beetle | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Mallet, M. A., J. M. | 2011 | Experimental | Yes | Yes | No | | No | 3 | No SS+ and SS- |
| Bouchard, C. M. | | mutation-accumulation | | | | | | | treatments |
| Kimber and A. K. | | on the X chromosome | | | | | | | |
| Chippindale | | of Drosophila | | | | | | | |
| | | melanogaster reveals | | | | | | | |
| | | stronger selection | | | | | | | |
| | | on males than | | | | | | | |
| | | females | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Mallet, M. A. and A. | 2011 | Inbreeding reveals | No | | | | No | 1 | Mutation levels |
| K. Chippindale | | stronger net | | | | | | | analysed |
| | | selection on | | | | | | | |
| | | Drosophila | | | | | | | |
| | | melanogaster males: | | | | | | | |
| | | implications for | | | | | | | |
| | | mutation load and | | | | | | | |
| | | the fitness of | | | | | | | |
| | | sexual females | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Martin, O. Y. and D. | 2003 | Costs and benefits | Yes | Yes | Yes | Yes | Yes | | Crossing took place |
| J. Hosken | | of evolving under | | | | | | | after Gen 29, |
| | | experimentally | | | | | | | results still |
| | | enforced polyandry | | | | | | | contain fitness |
| | | or monogamy | | | | | | | components though |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Martin, O. Y. and D. | 2004 | Reproductive | Yes | Yes | Yes | Yes | Yes | | Crossing also took |
| J. Hosken | | consequences of | | | | | | | place, it should |
| | | population | | | | | | | still be fine as |
| | | divergence through | | | | | | | they some |
| | | sexual conflict | | | | | | | populations were not |
| | | | | | | | | | crossed |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Matsuyama, T. and H. | 2009 | Mating time and call | Yes | Yes | No | | No | 3 | Mate choice in |
| Kuba | | frequency of males | | | | | | | different |
| | | between mass-reared | | | | | | | populations |
| | | and wild strains of | | | | | | | |
| | | melon fly, | | | | | | | |
| | | Bactrocera | | | | | | | |
| | | cucurbitae | | | | | | | |
| | | (Coquillett) | | | | | | | |
| | | (Diptera: | | | | | | | |
| | | Tephritidae) | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| McGuigan, K., D. | 2011 | REDUCING MUTATION | Yes | Yes | Yes | Yes | Yes | | The control line was |
| Petfield and M. W. | | LOAD THROUGH SEXUAL | | | | | | | not enforced |
| Blows | | SELECTION ON MALES | | | | | | | monomagous (did not |
| | | | | | | | | | remove SS)., it was |
| | | | | | | | | | just a control where |
| | | | | | | | | | the population was |
| | | | | | | | | | mutagenised. No |
| | | | | | | | | | clear SS treatment |
| | | | | | | | | | as level of |
| | | | | | | | | | selection varied |
| | | | | | | | | | across the |
| | | | | | | | | | generations. |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| McKean, K. A. and L. | 2008 | Sexual selection and | Yes | Yes | Yes | Yes | Yes | | The control line was |
| Nunney | | immune function in | | | | | | | a 1:1 SR but not |
| | | Drosophila | | | | | | | enforced monogamy |
| | | melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| McLain, D. K. | 1992 | Population density | No | | | | No | 1 | Field study |
| | | and the intensity of | | | | | | | |
| | | sexual selection on | | | | | | | |
| | | body length in | | | | | | | |
| | | spatially or | | | | | | | |
| | | temporally | | | | | | | |
| | | restricted natural | | | | | | | |
| | | populations of a | | | | | | | |
| | | seed bug | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| McNamara, K. B., S. | 2016 | Male-biased sex | Yes | Yes | Yes | Yes | Yes | | No SS- (enforced |
| P. Robinson, M. E. | | ratio does not | | | | | | | monogamy) just |
| Rosa, N. S. Sloan, | | promote increased | | | | | | | altered SR |
| E. van Lieshout and | | sperm | | | | | | | |
| L. W. Simmons | | competitiveness in | | | | | | | |
| | | the seed beetle, | | | | | | | |
| | | Callosobruchus | | | | | | | |
| | | maculatus | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| McNamara, K. B., E. | 2014 | A test of the | Yes | Yes | Yes | Yes | Yes | | Polygamy was still |
| van Lieshout and L. | | sexy-sperm and | | | | | | | randomly done |
| W. Simmons | | good-sperm | | | | | | | meaning post-cop SS |
| | | hypotheses for the | | | | | | | is only available. |
| | | evolution of | | | | | | | Numorous measures of |
| | | polyandry | | | | | | | fitness conducted |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Meffert, L. M., J. | 2006 | Testing alternative | Yes | Yes | No | | No | 3 | No tsts of SS |
| L. Regan, S. K. | | methods for purging | | | | | | | |
| Hicks, N. Mukana and | | genetic load using | | | | | | | |
| S. B. Day | | the housefly (Musca | | | | | | | |
| | | domestica L.) | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Michalczyk, L., A. | 2011 | Inbreeding Promotes | Yes | Yes | No | | No | 3 | It does not appear |
| L. Millard, O. Y. | | Female Promiscuity | | | | | | | the SS regimes were |
| Martin, A. J. | | | | | | | | | enforced (fig 1) |
| Lumley, B. C. | | | | | | | | | |
| Emerson, T. Chapman | | | | | | | | | |
| and M. J. G. Gage | | | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Michalczyk, L., A. | 2011 | Experimental | Yes | Yes | Yes | Yes | Yes | | No enforced monogamy |
| L. Millard, O. Y. | | Evolution Exposes | | | | | | | (no SS-), but |
| Martin, A. J. | | Female and Male | | | | | | | different OSR |
| Lumley, B. C. | | Responses to Sexual | | | | | | | |
| Emerson and M. J. G. | | Selection and | | | | | | | |
| Gage | | Conflict in | | | | | | | |
| | | Tribolium Castaneum | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Morrow, E. H., A. D. | 2008 | Assessing the extent | Yes | Yes | No | | No | 3 | Not assessing SS |
| Stewart and W. R. | | of genome-wide | | | | | | | |
| Rice | | intralocus sexual | | | | | | | |
| | | conflict via | | | | | | | |
| | | experimentally | | | | | | | |
| | | enforced | | | | | | | |
| | | gender-limited | | | | | | | |
| | | selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Nandy, B., P. | 2013 | Sperm Competitive | Yes | Yes | Yes | Yes | Yes | | Use an OSR of male |
| Chakraborty, V. | | Ability Evolves in | | | | | | | and female bias |
| Gupta, S. Z. Ali and | | Response to | | | | | | | |
| N. G. Prasad | | Experimental | | | | | | | |
| | | Alteration of | | | | | | | |
| | | Operational Sex | | | | | | | |
| | | Ratio | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Nandy, B., V. Gupta, | 2014 | Experimental | Yes | Yes | No | Yes | Yes | | Use an OSR of male |
| N. Udaykumar, M. A. | | Evolution of Female | | | | | | | and female bias |
| Samant, S. Sen and | | Traits under | | | | | | | |
| N. G. Prasad | | Different Levels of | | | | | | | |
| | | Intersexual Conflict | | | | | | | |
| | | in Drosophila | | | | | | | |
| | | Melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Nelson, A. C., K. E. | 2013 | Rapid adaptation to | Yes | Yes | Yes | Yes | Yes | | 3 generations in |
| Colson, S. Harmon | | mammalian sociality | | | | | | | mice with direct |
| and W. K. Potts | | via sexually | | | | | | | fitness outcomes |
| | | selected traits | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Nie, H. and K. | 2016 | Sexual selection and | No | | | | No | 1 | Artificial selection |
| Kaneshiro | | incipient speciation | | | | | | | was conducted |
| | | in Hawaiian | | | | | | | alongside mate |
| | | Drosophila | | | | | | | choice |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Palopoli, M. F., C. | 2015 | Natural and | Yes | No | | | No | 2b | Hermaphroditic, also |
| Peden, C. Woo, K. | | experimental | | | | | | | competition not SS |
| Akiha, M. Ary, L. | | evolution of sexual | | | | | | | was modulated |
| Cruze, J. L. | | conflict within | | | | | | | |
| Anderson and P. C. | | Caenorhabditis | | | | | | | |
| Phillips | | nematodes | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Partridge, L. | 1980 | Mate Choice | Yes | Yes | Yes | Yes | Yes | | Competitive success |
| | | Increases a | | | | | | | from 1 generation of |
| | | Component of | | | | | | | populations with and |
| | | Offspring Fitness in | | | | | | | without mate choice |
| | | Fruit-Flies | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Pelabon, C., L. K. | 2014 | The effects of | Yes | Yes | Yes | Yes | Yes | | Direct and indirect |
| Larsen, G. H. | | sexual selection on | | | | | | | outcomes |
| Bolstad, A. Viken, | | life-history traits: | | | | | | | |
| I. A. Fleming and G. | | An experimental | | | | | | | |
| Rosenqvist | | study on guppies | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Perry, J. C., R. | 2016 | Experimental | Yes | Yes | No | | No | 3 | SS was manipulated |
| Joag, D. J. Hosken, | | evolution under | | | | | | | with SPR not |
| N. Wedell, J. Radwan | | hyper-promiscuity in | | | | | | | enforced selection |
| and S. Wigby | | Drosophila | | | | | | | conditions |
| | | melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Pischedda, A. and A. | 2005 | Sex, mutation and | No | | | | No | 1 | Effect of mutation |
| Chippindale | | fitness: asymmetric | | | | | | | in diff populations |
| | | costs and routes to | | | | | | | |
| | | recovery through | | | | | | | |
| | | compensatory | | | | | | | |
| | | evolution | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Pischedda, A. and A. | 2006 | Intralocus sexual | No | | | | No | 1 | Focussed on fitness |
| K. Chippindale | | conflict diminishes | | | | | | | effects of conflict |
| | | the benefits of | | | | | | | |
| | | sexual selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Pitnick, S., W. D. | 2001 | Evolution of female | Yes | Yes | Yes | Yes | Yes | | Body size and number |
| Brown and G. T. | | remating behaviour | | | | | | | of progeny measured. |
| Miller | | following | | | | | | | Not purpose of study |
| | | experimental removal | | | | | | | though |
| | | of sexual selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Pitnick, S., G. T. | 2001 | Males' evolutionary | Yes | Yes | Yes | Yes | Yes | | male and population |
| Miller, J. Reagan | | responses to | | | | | | | fitness outcomes |
| and B. Holland | | experimental removal | | | | | | | |
| | | of sexual selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Plesnar, A., M. | 2011 | The role of sexual | Yes | Yes | Yes | Yes | Yes | | |
| Konior and J. Radwan | | selection in purging | | | | | | | |
| | | the genome of | | | | | | | |
| | | induced mutations in | | | | | | | |
| | | the bulb mite | | | | | | | |
| | | (Rizoglyphus robini) | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Plesnar-Bielak, A., | 2013 | No Evidence for | Yes | Yes | Yes | No | No | 4 | Reproductive |
| A. M. Skrzynecka, Z. | | Reproductive | | | | | | | isolation measured |
| M. Prokop, M. | | Isolation through | | | | | | | without fitness |
| Kolasa, M. Dzia_o | | Sexual Conflict in | | | | | | | components |
| and J. Radwan | | the Bulb Mite | | | | | | | |
| | | Rhizoglyphus robini | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Plesnar-Bielak, A., | 2012 | Mating system | Yes | Yes | Yes | Yes | Yes | | |
| A. M. Skrzynecka, Z. | | affects population | | | | | | | |
| M. Prokop and J. | | performance and | | | | | | | |
| Radwan | | extinction risk | | | | | | | |
| | | under environmental | | | | | | | |
| | | challenge | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Power, D. J. and L. | 2014 | Polyandrous females | Yes | Yes | Yes | Yes | Yes | | Remating was |
| Holman | | found fitter | | | | | | | presented to the |
| | | populations | | | | | | | females 72 hours |
| | | | | | | | | | after first mating. |
| | | | | | | | | | Measuring effects of |
| | | | | | | | | | polyandry, thus |
| | | | | | | | | | multiple mating has |
| | | | | | | | | | more of an effect. |
| | | | | | | | | | Post copulatory |
| | | | | | | | | | selection will take |
| | | | | | | | | | place though. |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Power, D. J. and L. | 2015 | Assessing the | Yes | Yes | Yes | Yes | Yes | | Experiment 2 |
| Holman | | alignment of sexual | | | | | | | Measures affect of |
| | | and natural | | | | | | | SS |
| | | selection using | | | | | | | |
| | | radiomutagenized | | | | | | | |
| | | seed beetles | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Price, T. A. R., G. | 2010 | Polyandry Prevents | Yes | Yes | No | | No | 3 | Appears that |
| D. D. Hurst and N. | | Extinction | | | | | | | individuals that |
| Wedell | | | | | | | | | only mated once |
| | | | | | | | | | still had a choice, |
| | | | | | | | | | post cop SS would be |
| | | | | | | | | | enacted then. |
| | | | | | | | | | Interested in mating |
| | | | | | | | | | freq over choice |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Prokop, Z. M., M. A. | 2017 | Do males pay for | No | | | | No | 1 | SS was estimated |
| Prus, T. S. | | sex? Sex-specific | | | | | | | using models |
| Gaczorek, K. Sychta, | | selection | | | | | | | |
| J. K. Palka, A. | | coefficients suggest | | | | | | | |
| Plesnar-Bielak and | | not | | | | | | | |
| M. Skarbo_ | | | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Promislow, D. E. L., | 1998 | Adult fitness | Yes | Yes | Yes | Yes | Yes | | |
| E. A. Smith and L. | | consequences of | | | | | | | |
| Pearse | | sexual selection in | | | | | | | |
| | | Drosophila | | | | | | | |
| | | melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Radwan, J. | 2004 | Effectiveness of | Yes | Yes | Yes | Yes | Yes | | Direct fitness |
| | | sexual selection in | | | | | | | outcomes measured |
| | | removing mutations | | | | | | | |
| | | induced with | | | | | | | |
| | | ionizing radiation | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Radwan, J., J. | 2004 | Effectiveness of | Yes | Yes | Yes | Yes | Yes | | Direct fitness |
| Unrug, K. Snigorska | | sexual selection in | | | | | | | outcomes measured |
| and K. Gawronska | | preventing fitness | | | | | | | |
| | | deterioration in | | | | | | | |
| | | bulb mite | | | | | | | |
| | | populations under | | | | | | | |
| | | relaxed natural | | | | | | | |
| | | selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Rundle, H. D., S. F. | 2006 | The roles of natural | Yes | Yes | Yes | Yes | Yes | | Direct fitness |
| Chenoweth and M. W. | | and sexual selection | | | | | | | outcomes measured |
| Blows | | during adaptation to | | | | | | | |
| | | a novel environment | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Rundle, H. D., S. F. | 2009 | The diversification | Yes | Yes | Yes | No | No | 4 | CHCs / mate |
| Chenoweth and M. W. | | of mate preferences | | | | | | | preference outcome |
| Blows | | by natural and | | | | | | | measured alongside |
| | | sexual selection | | | | | | | natural selection |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Rundle, H. D., A. | 2007 | An experimental test | Yes | Yes | No | | No | 3 | Between studs and |
| Odeen and A. O. | | for indirect | | | | | | | duds not SS+ / SS- |
| Mooers | | benefits in | | | | | | | |
| | | Drosophila | | | | | | | |
| | | melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Savic Veselinovic, | 2013 | Sexual Selection Can | Yes | Yes | Yes | Yes | No | No ES | Irradiated and |
| M., S. | | Reduce Mutational | | | | | | | direct fitness |
| Pavkovic-Lucic, Z. | | Load in Drosophila | | | | | | | outcomes measured |
| Kurbalija Novicic, | | Subobscura | | | | | | | |
| M. Jelic and M. | | | | | | | | | |
| Andelkovic | | | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| _e_lija, D., I. | 2008 | Sexual selection and | Yes | Yes | No | | No | 3 | While there is |
| Marecko and N. Tucic | | senescence: Do seed | | | | | | | monoandrous lines, |
| | | beetle males | | | | | | | these lines were not |
| | | (Acanthoscelides | | | | | | | enforced and choice |
| | | obtectus, Bruchidae, | | | | | | | still existed. Put |
| | | Coleoptera) shape | | | | | | | post-cop choice may |
| | | the longevity of | | | | | | | be stronger in other |
| | | their mates? | | | | | | | lines. This is a |
| | | | | | | | | | strange setup and |
| | | | | | | | | | may be hard to |
| | | | | | | | | | compare with other |
| | | | | | | | | | studies |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Sharma, M. D., J. | 2012 | Antagonistic | Yes | Yes | Yes | No | No | 4 | CHCs / mate |
| Hunt and D. J. | | Responses to Natural | | | | | | | preference outcome |
| Hosken | | and Sexual Selection | | | | | | | measured alongside |
| | | and the Sex-Specific | | | | | | | natural selection |
| | | Evolution of | | | | | | | |
| | | Cuticular | | | | | | | |
| | | Hydrocarbons in | | | | | | | |
| | | Drosophila Simulans | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Sharp, N. P. and A. | 2008 | Mating density and | No | | | | No | 1 | One generation w/ |
| F. Agrawal | | the strength of | | | | | | | gene freq. Also no |
| | | sexual selection | | | | | | | enforced monogamy |
| | | against deleterious | | | | | | | |
| | | alleles in | | | | | | | |
| | | Drosophila | | | | | | | |
| | | melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Sharp, N. P. and A. | 2009 | Sexual Selection and | No | | | | No | 1 | Assortive mating |
| F. Agrawal | | the Random Union of | | | | | | | study |
| | | Gametes: Testing for | | | | | | | |
| | | a Correlation in | | | | | | | |
| | | Fitness between | | | | | | | |
| | | Mates in Drosophila | | | | | | | |
| | | melanogaster | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Simmons, L. W. and | 2014 | Experimental | Yes | Yes | Yes | No | No | 4 | States that "Far |
| R. C. Firman | | Evidence for the | | | | | | | less is known of the |
| | | Evolution of the | | | | | | | fitness consequences |
| | | Mammalian Baculum by | | | | | | | of variation in |
| | | Sexual Selection | | | | | | | baculum morphology |
| | | | | | | | | | for mammals." - No |
| | | | | | | | | | direct link with |
| | | | | | | | | | fitness advantage. |
| | | | | | | | | | However the size is |
| | | | | | | | | | cited as having a |
| | | | | | | | | | fitness advantage |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Simmons, L. W. and | 2008 | Evolutionary | Yes | Yes | Yes | Yes | Yes | | Direct fitness |
| F. Garcia-Gonzalez | | Reduction in Testes | | | | | | | outcomes measured |
| | | Size and Competitive | | | | | | | |
| | | Fertilization | | | | | | | |
| | | Success in Response | | | | | | | |
| | | to the Experimental | | | | | | | |
| | | Removal of Sexual | | | | | | | |
| | | Selection in Dung | | | | | | | |
| | | Beetles | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Simmons, L. W. and | 2011 | Experimental | Yes | Yes | Yes | No | No | 4 | Genital morphology |
| F. Garcia-Gonzalez | | coevolution of male | | | | | | | has conflicting |
| | | and female genital | | | | | | | fitness outcomes for |
| | | morphology | | | | | | | males and females |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Simmons, L. W., C. | 2009 | Evolutionary | Yes | Yes | Yes | No | No | 4 | Genital Morphology |
| M. House, J. Hunt | | Response to Sexual | | | | | | | |
| and F. | | Selection in Male | | | | | | | |
| Garcia-Gonzalez | | Genital Morphology | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Snook, R. R., N. A. | 2013 | Sexual selection and | Yes | Yes | Yes | No | No | 4 | In D. pseudo |
| Gidaszewski, T. | | the evolution of | | | | | | | monogamy was |
| Chapman and L. W. | | secondary sexual | | | | | | | enforced. Sex combs |
| Simmons | | traits: sex comb | | | | | | | are cited as having |
| | | evolution in | | | | | | | positive fitness |
| | | Drosophila | | | | | | | effects at high and |
| | | | | | | | | | low numbers. Would |
| | | | | | | | | | not give an accurate |
| | | | | | | | | | representation of a |
| | | | | | | | | | fitness comparison |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Tilszer, M., K. | 2006 | Evolution under | Yes | Yes | Yes | Yes | Yes | | |
| Antoszczyk, N. | | relaxed sexual | | | | | | | |
| Sa_ek, E. Zajac and | | conflict in the bulb | | | | | | | |
| J. Radwan | | mite Rhizoglyphus | | | | | | | |
| | | robini | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| van Lieshout, E., K. | 2014 | Rapid Loss of | Yes | Yes | Yes | Yes | Yes | | Did not use enforced |
| B. McNamara and L. | | Behavioral | | | | | | | monogamy but had |
| W. Simmons | | Plasticity and | | | | | | | different OSR |
| | | Immunocompetence | | | | | | | |
| | | under Intense Sexual | | | | | | | |
| | | Selection | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Whitlock, M. C. and | 2000 | Factors affecting | Yes | Yes | No | | No | 3 | No manipulation of |
| D. Bourguet | | the genetic load in | | | | | | | SS |
| | | Drosophila: | | | | | | | |
| | | Synergistic | | | | | | | |
| | | epistasis and | | | | | | | |
| | | correlations among | | | | | | | |
| | | fitness components | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+
| Wigby, S. and T. | 2004 | Female resistance to | Yes | Yes | Yes | Yes | Yes | | Did not use enforced |
| Chapman | | male harm evolves in | | | | | | | monogamy but had |
| | | response to | | | | | | | different SR |
| | | manipulation of | | | | | | | |
| | | sexual conflict | | | | | | | |
+----------------------+------+-----------------------+--------------+------------+--------------------------+----------+----------+------------------+----------------------+Data Extraction
The raw extracted data table is presented in the accompanying data folder. It details the type of data collected for each study (arithmatic means, SD, n, F-statistic, chi-squared, proportion etc.). The rules utilised were as follows:
Arithmatic means, standard deviations/errors and sample sizes were extracted from a paper, supplementary material or a linked data repository (e.g. Data Dryad). This was possible when means and SD were reported in text or in a table. We would preferentially extract data for each experimental evolution line/replicat/family if possible and only extract data for the final reported generation (which was noted down).
If we could not find the means and SD in text format we used web-plot digitizer (v.3.12) to extract data from graphs.
If means were not reported then we ecxtracted a summary statistic or proportion value, which we could later convert to Hedges g’ using the compute.es package. Summary statistics included F, z, t and chi2. These conversions still required providing sample sizes for each treatment so these needed to be extractable from the study. Some summary statistics were obtained from generalized linear model summary tabels, others from straight forward ANOVAs and then some from more complex analysis such as proportional hazards statistical tests.
The covariates collected were extensive (DATA TABLE) and are discussed later. They were all regarded as potentially explaining trends in effect size or bias and hence were collected.
Effect Size Calculation
Not sure what to do about these calculations. I did not really lay them out neatly and the compute.es does not really return neat values. My calculations were done in a clunkier manual way. And are shown below.
SS- will be group one with increased SS being group two
Almbro and Simmons 2014**
Brood Number (No Distinction)
mes(15.4, 18, 10.071, 10.071, 207, 207, dig = 3)Mean Differences ES:
d [ 95 %CI] = -0.258 [ -0.452 , -0.064 ]
var(d) = 0.01
p-value(d) = 0.009
U3(d) = 39.814 %
CLES(d) = 42.757 %
Cliff's Delta = -0.145
g [ 95 %CI] = -0.258 [ -0.451 , -0.064 ]
var(g) = 0.01
p-value(g) = 0.009
U3(g) = 39.832 %
CLES(g) = 42.771 %
Correlation ES:
r [ 95 %CI] = -0.128 [ -0.222 , -0.032 ]
var(r) = 0.002
p-value(r) = 0.009
z [ 95 %CI] = -0.129 [ -0.226 , -0.032 ]
var(z) = 0.002
p-value(z) = 0.009
Odds Ratio ES:
OR [ 95 %CI] = 0.626 [ 0.44 , 0.89 ]
p-value(OR) = 0.009
Log OR [ 95 %CI] = -0.468 [ -0.82 , -0.116 ]
var(lOR) = 0.032
p-value(Log OR) = 0.009
Other:
NNT = -15.555
Total N = 414Strength (Stressed)
mes(0.047, 0.094, 0.05724, 0.16217, 91, 91, dig = 3)Mean Differences ES:
d [ 95 %CI] = -0.386 [ -0.682 , -0.091 ]
var(d) = 0.022
p-value(d) = 0.011
U3(d) = 34.956 %
CLES(d) = 39.231 %
Cliff's Delta = -0.215
g [ 95 %CI] = -0.385 [ -0.679 , -0.091 ]
var(g) = 0.022
p-value(g) = 0.011
U3(g) = 35.016 %
CLES(g) = 39.275 %
Correlation ES:
r [ 95 %CI] = -0.19 [ -0.327 , -0.045 ]
var(r) = 0.005
p-value(r) = 0.011
z [ 95 %CI] = -0.192 [ -0.34 , -0.045 ]
var(z) = 0.006
p-value(z) = 0.011
Odds Ratio ES:
OR [ 95 %CI] = 0.496 [ 0.29 , 0.847 ]
p-value(OR) = 0.011
Log OR [ 95 %CI] = -0.701 [ -1.237 , -0.166 ]
var(lOR) = 0.074
p-value(Log OR) = 0.011
Other:
NNT = -11.074
Total N = 182The Meta-Analysis Dataset
Setup
Read in csv file
prelim.data <- read.csv('Preliminary data frame 22.2.18.csv')
kable(prelim.data)| Study.ID | Group.ID | Authors | Year | Species | Taxon | SS.density.high.to.low | SS.ratio.high | SS.density.high | Ratio.Category | Density.Category | SSS.Categorical | Pre.cop | Post.cop | Blinding | Generations | Enforced.Monogamy | n | Outcome | Sex | Ambiguous | Outcome.Class | Environment | g | var.g | mean.low | sd.low | n.low | mean.high | sd.high | n.high | JIF |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 37 | Almbro, M. and L. W. Simmons | 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 3 | YES | 182 | Strength | M | NO | Indirect | Stressed | 0.385 | 0.022 | 0.0470000 | 0.0572364 | 91 | 0.0940000 | 0.1621697 | 91 | 4.612 |
| 1 | 37 | Almbro, M. and L. W. Simmons | 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 3 | YES | 182 | Strength | M | NO | Indirect | Unstressed | 0.000 | 0.022 | 0.1170000 | 0.1717091 | 91 | 0.1170000 | 0.1717091 | 91 | 4.612 |
| 1 | 37 | Almbro, M. and L. W. Simmons | 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 3 | YES | 222 | Ejaculate Quality and Production | M | NO | Indirect | Stressed | 0.172 | 0.018 | 1.8920000 | 0.9060662 | 111 | 2.0510000 | 0.9376732 | 111 | 4.612 |
| 1 | 37 | Almbro, M. and L. W. Simmons | 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 3 | YES | 222 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.204 | 0.018 | 2.1900000 | 0.9692801 | 111 | 2.3820000 | 0.9060662 | 111 | 4.612 |
| 1 | 37 | Almbro, M. and L. W. Simmons | 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 2 | YES | 414 | Reproductive Success | F | NO | Direct | Not Stated | 0.258 | 0.010 | 15.4000000 | 10.0712462 | 207 | 18.0000000 | 10.0712462 | 207 | 4.612 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | -0.011 | 0.010 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | 0.434 | 0.010 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | -0.064 | 0.010 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | -0.037 | 0.010 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | -0.129 | 0.010 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | 0.032 | 0.010 | NA | NA | NA | NA | NA | NA | 4.864 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Lifespan | M | NO | Indirect | Stressed | -0.971 | 0.005 | 30.5200000 | 5.9396970 | 450 | 24.2100000 | 7.0003571 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Lifespan | F | NO | Indirect | Stressed | -0.154 | 0.004 | 34.2800000 | 26.9407684 | 450 | 31.3200000 | 4.0305087 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Fitness Senescence | M | NO | Indirect | Stressed | 0.074 | 0.004 | 3.6300000 | 1.2727922 | 450 | 3.4300000 | 3.6062446 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Fitness Senescence | F | NO | Indirect | Stressed | -0.087 | 0.004 | 3.9200000 | 1.4849242 | 450 | 4.3500000 | 6.7882251 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Offspring Viability | M | NO | Direct | Stressed | -0.868 | 0.005 | 0.0295858 | 0.0063640 | 450 | 0.0372000 | 0.0106066 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Offspring Viability | F | NO | Direct | Stressed | -0.148 | 0.004 | 0.0264000 | 0.0254558 | 450 | 0.0291000 | 0.0042426 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Lifespan | M | NO | Indirect | Unstressed | -0.780 | 0.005 | 35.5500000 | 11.0308658 | 450 | 26.9400000 | 11.0308658 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Lifespan | F | NO | Indirect | Unstressed | -0.146 | 0.004 | 34.2800000 | 26.9407684 | 450 | 30.1900000 | 28.8499567 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Fitness Senescence | M | NO | Indirect | Unstressed | 0.021 | 0.004 | 4.5000000 | 6.1518290 | 450 | 4.3300000 | 9.9702056 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Fitness Senescence | F | NO | Indirect | Unstressed | -0.038 | 0.004 | 4.8200000 | 5.9396970 | 450 | 5.1500000 | 10.8187337 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Offspring Viability | M | NO | Direct | Unstressed | -0.855 | 0.005 | 0.2580000 | 0.0042426 | 450 | 0.0339000 | 0.0127279 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 45 | YES | 900 | Offspring Viability | F | NO | Direct | Unstressed | -0.176 | 0.004 | 0.0267000 | 0.0169706 | 450 | 0.0305000 | 0.0254558 | 450 | 5.210 |
| 5 | 1 | Bernasconi, G. and L. Keller | 2001 | Tribolium castaneum | Beetle | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 3 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | 1.533 | 0.242 | 0.5600000 | 0.3600000 | 10 | 0.9700000 | 0.0400000 | 10 | 2.673 |
| 5 | 1 | Bernasconi, G. and L. Keller | 2001 | Tribolium castaneum | Beetle | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 3 | YES | 20 | Reproductive Success | F | NO | Direct | Unstressed | -0.123 | 0.184 | 63.0000000 | 27.0000000 | 10 | 60.0000000 | 19.0000000 | 10 | 2.673 |
| 6 | 15 | Brommer, J. E., C. Fricke, D. A. Edward and T. Chapman | 2012 | Drosophila melanogaster | Fly | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 4 | YES | 93 | Reproductive Success | B | NO | Direct | Unstressed | -0.378 | 0.043 | 1.0000000 | 0.3316625 | 44 | 0.8700000 | 0.3500000 | 49 | 4.864 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 21 | YES | 200 | Reproductive Success | F | NO | Direct | Stressed | -0.216 | 0.020 | 76.9000000 | 47.0000000 | 100 | 66.4000000 | 50.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 28 | YES | 200 | Reproductive Success | F | NO | Direct | Stressed | 0.280 | 0.020 | 120.6000000 | 119.0000000 | 100 | 153.6000000 | 116.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 28 | YES | 200 | Offspring Viability | F | NO | Direct | Stressed | 0.365 | 0.045 | 0.7810000 | 0.0700000 | 100 | 0.8740000 | 0.0500000 | 100 | 4.464 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 21 | YES | 200 | Reproductive Success | F | NO | Direct | Unstressed | -0.244 | 0.020 | 108.5000000 | 44.0000000 | 100 | 97.9000000 | 43.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 28 | YES | 200 | Reproductive Success | F | NO | Direct | Unstressed | 0.281 | 0.020 | 164.1000000 | 119.0000000 | 100 | 197.5000000 | 119.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 28 | YES | 200 | Offspring Viability | F | NO | Direct | Unstressed | -0.311 | 0.155 | 0.9680000 | 0.0400000 | 100 | 0.9450000 | 0.0400000 | 100 | 4.464 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 62 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | -0.168 | 0.184 | 15.7249071 | 1.9984654 | 10 | 15.3903346 | 1.7633519 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 61 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | -0.576 | 0.192 | 15.3903346 | 2.1160222 | 10 | 14.3122677 | 1.4106815 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 60 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.311 | 0.226 | 15.0185874 | 1.0580111 | 10 | 16.3940520 | 0.9404543 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 58 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 0.512 | 0.190 | 15.7992565 | 1.6457951 | 10 | 16.6542751 | 1.5282383 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 62 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.373 | 0.231 | 15.7249071 | 1.9984654 | 10 | 18.0669145 | 1.1755679 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 61 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.190 | 0.219 | 15.3903346 | 2.1160222 | 10 | 17.6208178 | 1.4106815 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 60 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.305 | 0.226 | 15.0185874 | 1.0580111 | 10 | 17.1003718 | 1.8809086 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 58 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.928 | 0.276 | 15.7992565 | 1.6457951 | 10 | 18.5873606 | 1.0580111 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 62 | NO | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.713 | 0.257 | 15.3903346 | 1.7633519 | 10 | 18.0700000 | 1.1755679 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 61 | NO | 10 | Mating Success | M | NO | Indirect | Unstressed | 2.248 | 0.310 | 14.3122677 | 1.4106815 | 10 | 17.6200000 | 1.4106815 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 60 | NO | 10 | Mating Success | M | NO | Indirect | Unstressed | 0.458 | 0.189 | 16.3940520 | 0.9404543 | 10 | 17.1000000 | 1.8809086 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 58 | NO | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.414 | 0.233 | 16.6542751 | 1.5282383 | 10 | 18.5900000 | 1.0580111 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 60 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.060 | 0.096 | 622.3853211 | 367.6177813 | 20 | 642.9357798 | 301.9717489 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 59 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | -0.089 | 0.096 | 760.3669725 | 407.0054007 | 20 | 733.9449541 | 354.4885748 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 57 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | -0.214 | 0.097 | 728.0733945 | 407.0054007 | 20 | 648.8073394 | 315.1009554 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 60 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.515 | 0.099 | 622.4000000 | 367.6177800 | 20 | 819.0825688 | 380.7469877 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 59 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.768 | 0.103 | 760.4000000 | 407.0054000 | 20 | 1200.7339450 | 682.7187366 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 57 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | 1.068 | 0.110 | 728.1000000 | 407.0054000 | 20 | 1150.8256880 | 367.6177813 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 60 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.503 | 0.099 | 642.9357798 | 301.9717489 | 20 | 819.0825688 | 380.7469877 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 59 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.841 | 0.105 | 733.9449541 | 354.4885748 | 20 | 1200.7339450 | 682.7187366 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 57 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 1.437 | 0.122 | 648.8073394 | 315.1009554 | 20 | 1150.8256880 | 367.6177813 | 20 | 5.429 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 55 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -0.861 | 0.111 | 237.3000000 | 55.0072700 | 20 | 169.5000000 | 95.8836795 | 18 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 54 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -0.655 | 0.118 | 210.6000000 | 67.6189300 | 17 | 170.5000000 | 50.7141992 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 55 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.026 | 0.123 | 0.5230000 | 0.1833600 | 20 | 0.5350000 | 0.2460732 | 18 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 54 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.360 | 0.140 | 0.4960000 | 0.1772900 | 17 | 0.6590000 | 0.2680019 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 55 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -1.447 | 0.132 | 237.3000000 | 55.0072700 | 20 | 150.0000000 | 63.4958266 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 54 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -0.739 | 0.114 | 210.6000000 | 67.6189300 | 17 | 154.1000000 | 80.6396305 | 19 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 55 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.620 | 0.160 | 0.5230000 | 0.1833600 | 20 | 0.7750000 | 0.2185246 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 54 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.450 | 0.140 | 0.4960000 | 0.1772900 | 17 | 0.6930000 | 0.2310216 | 19 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 55 | NO | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -0.233 | 0.110 | 169.5000000 | 95.8836795 | 18 | 150.0000000 | 63.4958266 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 54 | NO | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -0.235 | 0.107 | 170.5000000 | 50.7141992 | 17 | 154.1000000 | 80.6396305 | 19 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 55 | NO | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.590 | 0.160 | 0.5350000 | 0.2460732 | 18 | 0.7750000 | 0.2185246 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 54 | NO | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.080 | 0.150 | 0.6590000 | 0.2680019 | 17 | 0.6930000 | 0.2310216 | 19 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 55 | YES | 18 | Reproductive Success | F | NO | Direct | Unstressed | -0.520 | 0.105 | 500.3000000 | 174.4133000 | 20 | 403.8000000 | 261.3466663 | 18 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 54 | YES | 18 | Reproductive Success | F | NO | Direct | Unstressed | -0.843 | 0.123 | 474.7000000 | 195.0229000 | 17 | 315.4000000 | 173.5827468 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 55 | YES | 18 | Reproductive Success | F | NO | Direct | Unstressed | -2.487 | 0.188 | 403.8000000 | 261.3466663 | 18 | 228.1000000 | 152.5549081 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 54 | YES | 18 | Reproductive Success | F | NO | Direct | Unstressed | -1.065 | 0.122 | 315.4000000 | 173.5827468 | 17 | 266.1000000 | 188.3044344 | 19 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 55 | NO | 18 | Reproductive Success | F | NO | Direct | Unstressed | -0.796 | 0.118 | 500.3000000 | 174.4133000 | 20 | 228.1000000 | 152.5549081 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 54 | NO | 18 | Reproductive Success | F | NO | Direct | Unstressed | -0.266 | 0.108 | 474.7000000 | 195.0229000 | 17 | 266.1000000 | 188.3044344 | 19 | 3.636 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 98 | YES | 2038 | Body Size | M | YES | Ambiguous | Unstressed | 0.555 | 0.002 | 2.2200000 | 0.0730000 | 1019 | 2.2600000 | 0.0710000 | 1019 | 2.792 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 98 | YES | 2038 | Body Size | F | YES | Ambiguous | Unstressed | 0.111 | 0.002 | 2.4500000 | 0.0820000 | 1019 | 2.4600000 | 0.0980000 | 1019 | 2.792 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 98 | YES | 2038 | Mating Success | M | NO | Indirect | Unstressed | -0.663 | 0.004 | NA | NA | NA | NA | NA | NA | 2.792 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 98 | YES | 2038 | Mating Success | M | NO | Indirect | Unstressed | -0.655 | 0.004 | NA | NA | NA | NA | NA | NA | 2.792 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 98 | YES | 2038 | Mating Latency | M | YES | Indirect | Unstressed | -0.197 | 0.002 | 126.5000000 | 15.8000000 | 1019 | 129.4000000 | 13.5000000 | 1019 | 2.792 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 98 | YES | 2038 | Mating Latency | M | YES | Indirect | Unstressed | 2.486 | 0.003 | 153.8000000 | 19.7000000 | 1019 | 113.6000000 | 11.6000000 | 1019 | 2.792 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 36 | YES | 38 | Reproductive Success | F | NO | Direct | Stressed | 1.810 | 0.144 | 91.7000000 | 9.4400000 | 19 | 105.7700000 | 5.1700000 | 19 | 2.606 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 36 | YES | 38 | Reproductive Success | F | NO | Direct | Unstressed | 0.299 | 0.102 | 93.9700000 | 21.3500000 | 19 | 101.2400000 | 26.0600000 | 19 | 2.606 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 36 | YES | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.222 | 0.156 | 106.8500000 | 6.2000000 | 12 | 108.6500000 | 9.2000000 | 12 | 2.606 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 36 | YES | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.279 | 0.209 | 0.3000000 | 0.0500000 | 12 | 0.4200000 | 0.0500000 | 12 | 2.606 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 36 | YES | 44 | Offspring Viability | F | NO | Direct | Unstressed | 0.415 | 0.090 | 24.0000000 | 8.9442719 | 20 | 27.0000000 | 4.8989795 | 24 | 2.606 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 36 | YES | 45 | Offspring Viability | M | NO | Direct | Unstressed | 0.407 | 0.088 | 23.0000000 | 9.3808315 | 22 | 26.0000000 | 4.7958315 | 23 | 2.606 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | High | Medium | High | 1 | 1 | Not Blind | 70 | NO | 204 | Mating Latency | M | NO | Indirect | Stressed | -0.324 | 0.020 | 6.5230000 | 5.4190000 | 102 | 5.0170000 | 3.6600000 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | High | Medium | High | 1 | 1 | Not Blind | 70 | NO | 204 | Mating Duration | M | NO | Ambiguous | Stressed | 0.219 | 0.020 | 11.9500000 | 2.9810000 | 102 | 12.6440000 | 3.3310000 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | High | Medium | High | 1 | 1 | Not Blind | 70 | NO | 204 | Mating Latency | M | NO | Indirect | Unstressed | 0.099 | 0.019 | 5.5121951 | 3.5893711 | 102 | 5.8885017 | 3.9412702 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | High | Medium | High | 1 | 1 | Not Blind | 70 | NO | 204 | Mating Duration | M | NO | Ambiguous | Unstressed | 0.393 | 0.020 | 9.1892361 | 2.5424101 | 102 | 10.4565972 | 3.7697805 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | High | Medium | High | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | F | NO | Direct | Stressed | 0.070 | 0.019 | 72.3810000 | 35.0550000 | 102 | 74.8857645 | 35.9428779 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | High | Medium | High | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | F | NO | Direct | Stressed | 0.015 | 0.019 | 0.6410000 | 0.5090000 | 102 | 0.6491071 | 0.5545710 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | High | Medium | High | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | M | NO | Direct | Stressed | 0.001 | 0.019 | 0.7750000 | 0.6600000 | 102 | 0.7759516 | 0.7391160 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | High | Medium | High | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | F | NO | Direct | Unstressed | -0.312 | 0.020 | 81.9595782 | 34.6116602 | 102 | 71.0632689 | 35.0553994 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | High | Medium | High | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | F | NO | Direct | Unstressed | 0.099 | 0.019 | 0.5946429 | 0.5004665 | 102 | 0.6446429 | 0.5049752 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | High | Medium | High | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | M | NO | Direct | Unstressed | 0.049 | 0.019 | 0.7157439 | 0.6919384 | 102 | 0.7510381 | 0.7495999 | 102 | 8.090 |
| 13 | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | F | NO | Direct | Stressed | 0.396 | 0.080 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | F | NO | Direct | Stressed | -1.258 | 0.114 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | F | NO | Direct | Stressed | -0.352 | 0.076 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | F | NO | Direct | Stressed | 1.316 | 0.146 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | M | NO | Direct | Stressed | 1.196 | 0.132 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | M | NO | Direct | Stressed | 1.142 | 0.104 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | M | NO | Direct | Stressed | 0.131 | 0.072 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | M | NO | Direct | Stressed | 1.747 | 0.360 | NA | NA | NA | NA | NA | NA | 3.248 |
| 14 | 6 | Firman, R. C. | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 25 | YES | 30 | Male Attractiveness | M | NO | Indirect | Unstressed | -1.177 | 0.149 | NA | NA | NA | NA | NA | NA | 3.248 |
| 15 | 6 | Firman, R. C., L. Y. Cheam and L. W. Simmons | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 8 | YES | 54 | Ejaculate Quality and Production | M | NO | Indirect | Not Stated | 0.303 | 0.073 | NA | NA | NA | NA | NA | NA | 3.276 |
| 15 | 6 | Firman, R. C., L. Y. Cheam and L. W. Simmons | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 8 | YES | 54 | Ejaculate Quality and Production | M | NO | Indirect | Not Stated | 1.844 | 0.105 | NA | NA | NA | NA | NA | NA | 3.276 |
| 16 | 6 | Firman, R. C., F. Garcia-Gonzalez, E. Thyer, S. Wheeler, Z. Yamin, M. Yuan and L. W. Simmons | 2015 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Blind | 18 | YES | 60 | Ejaculate Quality and Production | M | NO | Indirect | Not Stated | 1.003 | 0.073 | 0.7010000 | 0.0492950 | 30 | 0.7470000 | 0.0438178 | 30 | 4.007 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 24 | YES | 88 | Reproductive Success | F | NO | Direct | Not Stated | -0.963 | 0.068 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 24 | YES | 41 | Reproductive Success | F | NO | Direct | Not Stated | -1.733 | 0.349 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 24 | YES | 78 | Reproductive Success | F | NO | Direct | Not Stated | -1.717 | 0.111 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 24 | YES | 55 | Reproductive Success | F | NO | Direct | Not Stated | -0.974 | 0.115 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 24 | YES | 86 | Reproductive Success | F | NO | Direct | Not Stated | -0.599 | 0.102 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 24 | YES | 55 | Reproductive Success | F | NO | Direct | Not Stated | -0.904 | 0.159 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 24 | YES | 36 | Reproductive Success | F | NO | Direct | Not Stated | -0.504 | 0.199 | NA | NA | NA | NA | NA | NA | 3.832 |
| 18 | 6 | Firman, R. C. and L. W. Simmons | 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 8 | YES | 144 | Ejaculate Quality and Production | M | NO | Indirect | Not Stated | 0.399 | 0.026 | NA | NA | NA | NA | NA | NA | 3.521 |
| 18 | 6 | Firman, R. C. and L. W. Simmons | 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 7 | YES | 40 | Reproductive Success | F | NO | Direct | Stressed | -0.564 | 0.100 | 17.5500000 | 5.4112845 | 20 | 14.4500000 | 5.3665631 | 20 | 3.521 |
| 18 | 6 | Firman, R. C. and L. W. Simmons | 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 7 | YES | 40 | Reproductive Success | F | NO | Direct | Unstressed | -0.328 | 0.097 | 16.1500000 | 4.1143651 | 20 | 14.5500000 | 5.3665631 | 20 | 3.521 |
| 18 | 6 | Firman, R. C. and L. W. Simmons | 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 10 | YES | 144 | Reproductive Success | F | NO | Direct | Not Stated | 0.668 | 0.029 | NA | NA | NA | NA | NA | NA | 3.521 |
| 18 | 6 | Firman, R. C. and L. W. Simmons | 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 8 | YES | 128 | Body Size | B | NO | Ambiguous | Not Stated | -0.364 | 0.031 | NA | NA | NA | NA | NA | NA | 3.521 |
| 19 | 6 | Firman, R. C. and L. W. Simmons | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 12 | YES | 128 | Reproductive Success | M | NO | Direct | Stressed | -1.008 | 0.035 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman, R. C. and L. W. Simmons | 2012 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 15 | YES | 144 | Reproductive Success | F | NO | Direct | Unstressed | 0.784 | 0.030 | 4.9400000 | 2.2910260 | 72 | 6.6500000 | 2.0364675 | 72 | 3.521 |
| 20 | 6 | Firman, R. C. and L. W. Simmons | 2012 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 15 | YES | 128 | Reproductive Success | F | NO | Direct | Unstressed | -0.213 | 0.031 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman, R. C. and L. W. Simmons | 2012 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 15 | YES | 128 | Reproductive Success | F | NO | Direct | Unstressed | 0.416 | 0.032 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman, R. C. and L. W. Simmons | 2012 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 15 | YES | 128 | Offspring Viability | B | NO | Direct | Unstressed | 0.014 | 0.031 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman, R. C. and L. W. Simmons | 2012 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 15 | YES | 128 | Offspring Viability | B | NO | Direct | Unstressed | 0.408 | 0.032 | NA | NA | NA | NA | NA | NA | 3.521 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 35 | YES | 155 | Body Size | F | YES | Ambiguous | Not Stated | 0.080 | 0.026 | 0.0011635 | 0.0001053 | 77 | 0.0011720 | 0.0001073 | 78 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 35 | YES | 155 | Body Size | M | YES | Ambiguous | Not Stated | 0.102 | 0.026 | 0.0009178 | 0.0000963 | 77 | 0.0009283 | 0.0001084 | 77 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 35 | YES | 76 | Development Rate | B | NO | Ambiguous | Stressed | -0.453 | 0.053 | 0.8289099 | 0.0286151 | 38 | 0.8135570 | 0.0377825 | 38 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 35 | YES | 79 | Development Rate | B | NO | Ambiguous | Unstressed | 0.772 | 0.053 | 0.8251609 | 0.0378719 | 39 | 0.8534363 | 0.0346177 | 40 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 35 | YES | 76 | Reproductive Success | F | NO | Direct | Stressed | -0.579 | 0.054 | 419.3947368 | 34.3546995 | 38 | 397.4210526 | 40.5573545 | 38 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 35 | YES | 79 | Reproductive Success | F | NO | Direct | Unstressed | 0.185 | 0.050 | 292.2051282 | 55.6900159 | 39 | 301.0500000 | 37.3678526 | 40 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 35 | YES | 76 | Offspring Viability | F | NO | Direct | Stressed | -0.476 | 0.053 | 0.5462428 | 0.0780889 | 38 | 0.5107408 | 0.0691831 | 38 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 35 | YES | 79 | Offspring Viability | F | NO | Direct | Unstressed | 0.543 | 0.052 | 0.4180605 | 0.0784570 | 39 | 0.4575765 | 0.0652579 | 40 | 4.502 |
| 23 | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | High | High | High | 1 | 1 | Blind | 20 | NO | 1200 | Reproductive Success | M | NO | Direct | Not Stated | -0.056 | 0.003 | NA | NA | NA | NA | NA | NA | 8.851 |
| 23 | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | High | High | High | 1 | 1 | Blind | 20 | NO | 1200 | Reproductive Success | F | NO | Direct | Not Stated | -0.031 | 0.003 | NA | NA | NA | NA | NA | NA | 8.851 |
| 23 | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | High | High | High | 1 | 1 | Blind | 20 | NO | 1200 | Lifespan | M | NO | Indirect | Not Stated | -0.066 | 0.003 | NA | NA | NA | NA | NA | NA | 8.851 |
| 23 | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | High | High | High | 1 | 1 | Blind | 20 | NO | 1200 | Lifespan | F | NO | Indirect | Not Stated | -0.083 | 0.003 | NA | NA | NA | NA | NA | NA | 8.851 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | High | Medium | High | 0 | 0 | Not Blind | 20 | NO | 90 | Ejaculate Quality and Production | M | NO | Indirect | Not Stated | -0.197 | 0.044 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | High | Medium | High | 0 | 0 | Not Blind | 20 | NO | 256 | Mating Success | M | NO | Indirect | Not Stated | -0.041 | 0.016 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | High | Medium | High | 0 | 0 | Not Blind | 20 | NO | 256 | Mating Success | M | NO | Indirect | Not Stated | -0.065 | 0.016 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | High | Medium | High | 0 | 0 | Not Blind | 20 | NO | 256 | Mating Success | M | NO | Indirect | Not Stated | -0.078 | 0.016 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | High | Medium | High | 0 | 0 | Not Blind | 20 | NO | 256 | Mating Success | M | NO | Indirect | Not Stated | -0.267 | 0.016 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | High | Medium | High | 0 | 0 | Not Blind | 20 | NO | 184 | Reproductive Success | B | NO | Direct | Not Stated | 0.095 | 0.022 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | High | Medium | High | 0 | 0 | Not Blind | 20 | NO | 184 | Reproductive Success | B | NO | Direct | Not Stated | 0.407 | 0.022 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | High | Medium | High | 0 | 0 | Not Blind | 20 | NO | 392 | Reproductive Success | B | NO | Direct | Not Stated | 0.059 | 0.010 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | High | Medium | High | 0 | 0 | Not Blind | 20 | NO | 392 | Reproductive Success | B | NO | Direct | Not Stated | 0.219 | 0.010 | NA | NA | NA | NA | NA | NA | 5.051 |
| 26 | 10 | Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady | 2009 | Callosobruchus maculatus | Beetle | 60.000 | 1.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 90 | YES | 80 | Body Size | M | YES | Ambiguous | Unstressed | 1.971 | 0.073 | 1.8700000 | 0.0822192 | 40 | 2.0400000 | 0.0885438 | 40 | 3.816 |
| 26 | 10 | Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady | 2009 | Callosobruchus maculatus | Beetle | 60.000 | 1.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 90 | YES | 80 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 1.385 | 0.061 | 0.4500000 | 0.1201666 | 40 | 0.6400000 | 0.1517893 | 40 | 3.816 |
| 26 | 10 | Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady | 2009 | Callosobruchus maculatus | Beetle | 60.000 | 1.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 90 | YES | 80 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.661 | 0.052 | 0.1571000 | 0.0059000 | 40 | 0.1626000 | 0.0103000 | 40 | 3.816 |
| 27 | 2 | Grazer, V. M., M. Demont, L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 39 | YES | 228 | Reproductive Success | B | NO | Direct | Stressed | 0.211 | 0.018 | 149.9000000 | 174.9000000 | 114 | 181.6000000 | 119.5000000 | 114 | 3.368 |
| 27 | 2 | Grazer, V. M., M. Demont, L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 39 | YES | 240 | Reproductive Success | B | NO | Direct | Unstressed | 0.214 | 0.017 | 240.6000000 | 189.7000000 | 120 | 291.5000000 | 275.6000000 | 120 | 3.368 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 49 | YES | 66 | Immunity | M | NO | Ambiguous | Unstressed | -0.141 | 0.059 | 6.9700000 | 1.7400000 | 33 | 6.7000000 | 2.0300000 | 33 | 2.591 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 49 | YES | 66 | Immunity | F | NO | Ambiguous | Unstressed | 0.848 | 0.065 | 6.3300000 | 1.3400000 | 33 | 7.7900000 | 2.0000000 | 33 | 2.591 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 49 | YES | 288 | Immunity | M | NO | Ambiguous | Stressed | 0.175 | 0.014 | 80.8600000 | 41.5400000 | 144 | 87.9200000 | 39.1000000 | 144 | 2.591 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 49 | YES | 288 | Immunity | F | NO | Ambiguous | Stressed | 0.089 | 0.014 | 85.0000000 | 41.5400000 | 144 | 88.9400000 | 46.4300000 | 144 | 2.591 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 49 | YES | 288 | Immunity | M | NO | Ambiguous | Unstressed | -0.097 | 0.014 | 92.8100000 | 35.8400000 | 144 | 89.2100000 | 38.2800000 | 144 | 2.591 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 49 | YES | 288 | Immunity | F | NO | Ambiguous | Unstressed | 0.070 | 0.014 | 87.9900000 | 35.8400000 | 144 | 90.2900000 | 29.3200000 | 144 | 2.591 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | M | NO | Ambiguous | Unstressed | -0.107 | 0.054 | 6.0300000 | 2.5400000 | 36 | 5.7500000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | F | NO | Ambiguous | Unstressed | 0.121 | 0.054 | 6.8100000 | 2.6200000 | 36 | 7.1300000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | B | NO | Ambiguous | Unstressed | -0.281 | 0.055 | 6.8400000 | 3.6700000 | 36 | 5.8200000 | 3.5000000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | M | NO | Ambiguous | Unstressed | -0.043 | 0.054 | 6.1400000 | 2.5400000 | 36 | 5.7500000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | F | NO | Ambiguous | Unstressed | -0.203 | 0.055 | 7.3400000 | 2.5400000 | 36 | 7.1300000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | B | NO | Ambiguous | Unstressed | -0.074 | 0.054 | 7.1100000 | 3.5000000 | 36 | 5.8200000 | 3.5000000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | M | NO | Ambiguous | Unstressed | -0.150 | 0.055 | 6.1400000 | 2.5400000 | 36 | 6.0300000 | 2.5400000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | F | NO | Ambiguous | Unstressed | -0.081 | 0.054 | 7.3400000 | 2.5400000 | 36 | 6.8100000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | B | NO | Ambiguous | Unstressed | -0.361 | 0.394 | 7.1100000 | 3.5000000 | 36 | 6.8400000 | 3.6700000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Stressed | -0.073 | 0.014 | 1.5000000 | 2.6800000 | 144 | 1.7100000 | 3.0500000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | F | NO | Ambiguous | Stressed | 0.035 | 0.014 | 1.4600000 | 2.9600000 | 144 | 1.3600000 | 2.7800000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Unstressed | -0.164 | 0.014 | 2.1100000 | 3.3300000 | 144 | 1.6200000 | 2.5900000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | F | NO | Ambiguous | Unstressed | 0.022 | 0.014 | 2.6900000 | 4.3500000 | 144 | 2.7900000 | 4.8100000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Stressed | 0.013 | 0.014 | 1.6700000 | 2.9600000 | 144 | 1.7100000 | 3.0500000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | F | NO | Ambiguous | Stressed | -0.025 | 0.014 | 1.4300000 | 2.7800000 | 144 | 1.3600000 | 2.7800000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Unstressed | -0.029 | 0.014 | 2.2100000 | 3.5200000 | 144 | 1.6200000 | 2.5900000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | F | NO | Ambiguous | Unstressed | 0.068 | 0.014 | 2.4100000 | 3.8900000 | 144 | 2.7900000 | 4.8100000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Stressed | -0.060 | 0.014 | 1.6700000 | 2.9600000 | 144 | 1.5000000 | 2.6800000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | F | NO | Ambiguous | Stressed | 0.010 | 0.014 | 1.4300000 | 2.7800000 | 144 | 1.4600000 | 2.9600000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Unstressed | -0.190 | 0.014 | 2.2100000 | 3.5200000 | 144 | 2.1100000 | 3.3300000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 56 | NO | 144 | Immunity | F | NO | Ambiguous | Unstressed | 0.087 | 0.014 | 2.4100000 | 3.8900000 | 144 | 2.6900000 | 4.3500000 | 144 | 3.264 |
| 30 | 17 | Holland, B. | 2002 | Drosophila melanogaster | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 38 | YES | 89 | Reproductive Success | F | NO | Direct | Stressed | -0.116 | 0.015 | 11.5900000 | 10.1800000 | 133 | 10.6600000 | 4.9500000 | 133 | 3.516 |
| 30 | 17 | Holland, B. | 2002 | Drosophila melanogaster | Fly | 2.500 | 4.00 | 5.00 | High | Low | Medium | 1 | 1 | Not Blind | 51 | YES | 89 | Reproductive Success | F | NO | Direct | Stressed | 0.070 | 0.015 | 14.4300000 | 3.2800000 | 133 | 14.8100000 | 6.9300000 | 133 | 3.516 |
| 31 | 18 | Holland, B. and W. R. Rice | 1999 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 47 | YES | 76 | Reproductive Success | F | NO | Direct | Stressed | -0.305 | 0.018 | 11.2400000 | 10.6600000 | 114 | 8.9300000 | 3.6000000 | 114 | 10.260 |
| 32 | 19 | Hollis, B., J. L. Fierst and D. Houle | 2009 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 8 | YES | 27 | Mutant Frequency | M | NO | Indirect | Stressed | 0.807 | 0.053 | NA | NA | NA | NA | NA | NA | 5.429 |
| 32 | 19 | Hollis, B., J. L. Fierst and D. Houle | 2009 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 8 | YES | 27 | Mutant Frequency | M | NO | Indirect | Unstressed | 0.237 | 0.049 | 0.9410000 | 1.7760000 | 40 | 0.3990000 | 2.6590000 | 40 | 5.429 |
| 33 | 19 | Hollis, B. and D. Houle | 2011 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 60 | YES | 120 | Reproductive Success | B | NO | Direct | Stressed | -0.304 | 0.011 | 126.5900000 | 28.9794410 | 180 | 117.6000000 | 29.1136051 | 180 | 3.276 |
| 33 | 19 | Hollis, B. and D. Houle | 2011 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 60 | YES | 164 | Reproductive Success | F | NO | Direct | Stressed | 0.031 | 0.008 | NA | NA | NA | NA | NA | NA | 3.276 |
| 33 | 19 | Hollis, B. and D. Houle | 2011 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 60 | YES | 164 | Offspring Viability | F | NO | Direct | Stressed | -0.064 | 0.008 | NA | NA | NA | NA | NA | NA | 3.276 |
| 34 | 19 | Hollis, B. and T. J. Kawecki | 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 100 | YES | 38 | Mating Latency | M | YES | Indirect | Stressed | 0.038 | 0.062 | NA | NA | NA | NA | NA | NA | 5.051 |
| 34 | 19 | Hollis, B. and T. J. Kawecki | 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 100 | YES | 90 | Mating Latency | M | YES | Indirect | Stressed | 0.194 | 0.043 | NA | NA | NA | NA | NA | NA | 5.051 |
| 34 | 19 | Hollis, B. and T. J. Kawecki | 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 100 | YES | 17 | Reproductive Success | M | NO | Direct | Stressed | 1.216 | 0.091 | 0.6010000 | 0.2950000 | 23 | 0.8760000 | 0.1380000 | 28 | 5.051 |
| 34 | 19 | Hollis, B. and T. J. Kawecki | 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 100 | YES | 21 | Reproductive Success | M | NO | Direct | Stressed | 0.659 | 0.066 | 0.5530000 | 0.3660000 | 30 | 0.7710000 | 0.2860000 | 33 | 5.051 |
| 34 | 19 | Hollis, B. and T. J. Kawecki | 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 100 | YES | 15 | Reproductive Success | M | NO | Direct | Stressed | 0.830 | 0.090 | 0.6100000 | 0.3400000 | 22 | 0.8530000 | 0.2300000 | 23 | 5.051 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 139 | YES | 48 | Development Rate | M | NO | Ambiguous | Stressed | -0.482 | 0.028 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 139 | YES | 48 | Development Rate | F | NO | Ambiguous | Stressed | 0.414 | 0.028 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 162 | YES | 60 | Body Size | M | YES | Ambiguous | Stressed | 0.000 | 0.022 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 162 | YES | 60 | Body Size | F | YES | Ambiguous | Stressed | -0.238 | 0.022 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 117 | YES | 44 | Fitness Senescence | M | YES | Indirect | Stressed | 0.500 | 0.031 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 117 | YES | 45 | Fitness Senescence | F | YES | Indirect | Stressed | 0.017 | 0.030 | NA | NA | NA | NA | NA | NA | 4.201 |
| 36 | 29 | Immonen, E., R. R. Snook and M. G. Ritchie | 2014 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | High | Low | Medium | 1 | 1 | Not Blind | 100 | YES | 30 | Reproductive Success | F | NO | Direct | Unstressed | 0.636 | 0.046 | NA | NA | NA | NA | NA | NA | 2.320 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | Low | High | Medium | 0 | 1 | Not Blind | 30 | NO | 110 | Body Size | M | YES | Ambiguous | Unstressed | -0.306 | 0.120 | 780.1295325 | 24.5249313 | 169 | 773.1405125 | 20.8806168 | 160 | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | Low | High | Medium | 0 | 1 | Not Blind | 30 | NO | 107 | Body Size | F | YES | Ambiguous | Unstressed | -0.290 | 0.013 | 879.0553188 | 25.2349182 | 160 | 870.6142500 | 32.5085570 | 160 | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | Low | High | Medium | 0 | 1 | Not Blind | 30 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.745 | 0.053 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | Low | High | Medium | 0 | 1 | Not Blind | 31 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.490 | 0.051 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | Low | High | Medium | 0 | 1 | Not Blind | 50 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.545 | 0.051 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | Low | High | Medium | 0 | 1 | Not Blind | 58 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.379 | 0.050 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | Low | High | Medium | 0 | 1 | Not Blind | 30 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | -0.228 | 0.049 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | Low | High | Medium | 0 | 1 | Not Blind | 31 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.300 | 0.050 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | Low | High | Medium | 0 | 1 | Not Blind | 50 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | -0.108 | 0.049 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | Low | High | Medium | 0 | 1 | Not Blind | 58 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.080 | 0.049 | NA | NA | NA | NA | NA | NA | 3.368 |
| 38 | 3 | Jacomb, F., J. Marsh and L. Holman | 2016 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Blind | 5 | YES | 320 | Pesticide Resistance | B | NO | Ambiguous | Stressed | 1.246 | 0.005 | 0.8560000 | 0.0210000 | 480 | 0.8920000 | 0.0350000 | 480 | 4.201 |
| 38 | 3 | Jacomb, F., J. Marsh and L. Holman | 2016 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Blind | 5 | YES | 176 | Pesticide Resistance | B | NO | Ambiguous | Unstressed | -1.001 | 0.005 | 0.0880000 | 0.0850000 | 480 | 0.0270000 | 0.0140000 | 48 | 4.201 |
| 39 | 32 | Jarzebowska, M. and J. Radwan | 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 8 | YES | 72 | Reproductive Success | F | NO | Direct | Stressed | 0.390 | 0.019 | 0.7390000 | 0.5240000 | 96 | 0.8080000 | 0.4010000 | 120 | 5.659 |
| 39 | 32 | Jarzebowska, M. and J. Radwan | 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 8 | YES | 72 | Reproductive Success | F | NO | Direct | Unstressed | -0.190 | 0.020 | 0.8350000 | 0.4730000 | 96 | 0.7880000 | 0.5730000 | 120 | 5.659 |
| 39 | 32 | Jarzebowska, M. and J. Radwan | 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 8 | YES | 11 | Extinction Rate | B | NO | Direct | Stressed | 0.752 | 0.133 | NA | NA | NA | NA | NA | NA | 5.659 |
| 39 | 32 | Jarzebowska, M. and J. Radwan | 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 8 | YES | 72 | Offspring Viability | B | NO | Direct | Stressed | 0.150 | 0.019 | 37.9100000 | 27.9900000 | 96 | 51.3200000 | 38.5200000 | 120 | 5.659 |
| 39 | 32 | Jarzebowska, M. and J. Radwan | 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 8 | YES | 72 | Offspring Viability | B | NO | Direct | Unstressed | -0.088 | 0.019 | 70.4400000 | 32.3000000 | 96 | 61.8700000 | 54.1700000 | 120 | 5.659 |
| 40 | 6 | Klemme, I. and R. C. Firman | 2013 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 18 | YES | 12 | Reproductive Success | M | NO | Direct | Stressed | 0.769 | 0.114 | 0.2800000 | 0.4200000 | 18 | 0.7200000 | 0.6700000 | 18 | 3.068 |
| 40 | 6 | Klemme, I. and R. C. Firman | 2013 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Not Blind | 18 | YES | 12 | Reproductive Success | M | NO | Direct | Unstressed | 0.946 | 0.119 | 0.3400000 | 0.3900000 | 18 | 0.7900000 | 0.5300000 | 18 | 3.068 |
| 41 | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Tribolium castaneum | Beetle | 1.000 | 9.00 | 100.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 56 | Reproductive Success | B | NO | Direct | Stressed | 0.576 | 0.025 | NA | NA | NA | NA | NA | NA | 38.138 |
| 41 | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 20 | YES | 16 | Reproductive Success | B | NO | Direct | Stressed | 0.559 | 0.084 | NA | NA | NA | NA | NA | NA | 38.138 |
| 41 | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Tribolium castaneum | Beetle | 1.000 | 9.00 | 100.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 56 | Extinction Rate | B | NO | Direct | Stressed | 0.522 | 0.024 | NA | NA | NA | NA | NA | NA | 38.138 |
| 41 | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 20 | YES | 16 | Extinction Rate | B | NO | Direct | Stressed | 0.798 | 0.087 | NA | NA | NA | NA | NA | NA | 38.138 |
| 42 | 11 | Maklakov, A. A., R. Bonduriansky and R. C. Brooks | 2009 | Callosobruchus maculatus | Beetle | 50.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | YES | 11 | Reproductive Success | F | NO | Direct | Not Stated | -0.958 | 0.133 | 155.1200000 | 37.7600000 | 16 | 118.0000000 | 37.7600000 | 16 | 5.429 |
| 43 | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 29 | YES | 10 | Lifespan | F | YES | Indirect | Unstressed | 0.841 | 0.138 | NA | NA | NA | NA | NA | NA | 3.833 |
| 43 | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 29 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 0.920 | 0.140 | NA | NA | NA | NA | NA | NA | 3.833 |
| 43 | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 29 | YES | 10 | Reproductive Success | F | NO | Direct | Unstressed | 1.038 | 0.144 | 28.2000000 | 15.4532035 | 15 | 49.2000000 | 23.1604404 | 15 | 3.833 |
| 43 | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 29 | YES | 10 | Lifespan | F | NO | Indirect | Stressed | -1.314 | 0.155 | 2.2130508 | 0.0600641 | 15 | 2.1161864 | 0.0817265 | 15 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 42 | YES | 12 | Reproductive Success | F | NO | Direct | Unstressed | 0.421 | 0.159 | 34.9043478 | 21.5075526 | 12 | 42.9391304 | 14.7600851 | 12 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 250.000 | 1.00 | 500.00 | Low | High | Medium | 1 | 1 | Not Blind | 42 | YES | 12 | Reproductive Success | F | NO | Direct | Unstressed | -0.075 | 0.155 | 34.9043478 | 21.5075526 | 12 | 33.4434783 | 15.6035186 | 12 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 10.000 | 1.00 | 500.00 | Low | High | Medium | 1 | 1 | Not Blind | 42 | NO | 12 | Reproductive Success | F | NO | Direct | Unstressed | -0.603 | 0.163 | 42.9391304 | 14.7600851 | 12 | 33.4434783 | 15.6035186 | 12 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 42 | YES | 24 | Lifespan | F | NO | Indirect | Unstressed | -0.405 | 0.082 | 17.3460898 | 2.4943223 | 24 | 16.3327787 | 2.4698682 | 24 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 250.000 | 1.00 | 500.00 | Low | High | Medium | 1 | 1 | Not Blind | 42 | YES | 24 | Lifespan | F | NO | Indirect | Unstressed | -0.638 | 0.085 | 17.3460898 | 2.4943223 | 24 | 15.8086522 | 2.2497809 | 24 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 10.000 | 1.00 | 500.00 | Low | High | Medium | 1 | 1 | Not Blind | 42 | NO | 24 | Lifespan | F | NO | Indirect | Unstressed | -0.216 | 0.081 | 16.3327787 | 2.4698682 | 24 | 15.8086522 | 2.2497809 | 24 | 3.833 |
| 45 | 33 | McGuigan, K., D. Petfield and M. W. Blows | 2011 | Drosophila serrata | Fly | 2.405 | 3.81 | 4.81 | High | Low | Medium | 1 | 0 | Not Blind | 23 | YES | 292 | Mating Success | M | NO | Indirect | Stressed | 0.034 | 0.014 | 0.4997000 | 0.3400000 | 146 | 0.5097000 | 0.2460000 | 146 | 5.146 |
| 45 | 33 | McGuigan, K., D. Petfield and M. W. Blows | 2011 | Drosophila serrata | Fly | 2.405 | 3.81 | 4.81 | High | Low | Medium | 1 | 0 | Not Blind | 26 | YES | 208 | Reproductive Success | F | NO | Direct | Stressed | 0.114 | 0.019 | 49.9300000 | 22.7000000 | 104 | 52.1740000 | 16.1000000 | 104 | 5.146 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | Low | High | High | 1 | 1 | Not Blind | 58 | NO | 40 | Body Size | B | YES | Ambiguous | Unstressed | 1.528 | 0.242 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | Low | High | High | 1 | 1 | Not Blind | 58 | NO | 40 | Development Rate | B | NO | Ambiguous | Unstressed | 0.853 | 0.105 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | Low | High | High | 1 | 1 | Not Blind | 58 | NO | 40 | Development Rate | B | NO | Ambiguous | Unstressed | 3.124 | 0.218 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | Low | High | High | 1 | 1 | Not Blind | 58 | NO | 40 | Development Rate | B | NO | Ambiguous | Unstressed | 2.655 | 0.184 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | Low | High | High | 1 | 1 | Not Blind | 58 | NO | 52 | Mating Success | M | NO | Indirect | Unstressed | 0.839 | 0.081 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | Low | High | High | 1 | 1 | Not Blind | 58 | NO | 52 | Mating Success | M | NO | Indirect | Unstressed | 1.598 | 0.099 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | Low | High | High | 1 | 1 | Not Blind | 58 | NO | 52 | Mating Success | M | NO | Indirect | Unstressed | 1.907 | 0.110 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | Low | High | High | 1 | 1 | Not Blind | 58 | NO | 80 | Immunity | B | NO | Ambiguous | Unstressed | -0.911 | 0.054 | NA | NA | NA | NA | NA | NA | 4.737 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 32 | NO | 153 | Ejaculate Quality and Production | M | YES | Indirect | Unstressed | -0.106 | 0.027 | 2.7000000 | 0.8442748 | 88 | 2.6000000 | 1.0480935 | 65 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 32 | NO | 145 | Ejaculate Quality and Production | M | YES | Indirect | Unstressed | -0.157 | 0.028 | 0.1600000 | 0.0728835 | 83 | 0.1500000 | 0.0472440 | 62 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 32 | NO | 202 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.100 | 0.020 | 0.5700000 | 0.1014889 | 103 | 0.5800000 | 0.0994987 | 99 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 32 | NO | 101 | Ejaculate Quality and Production | M | YES | Indirect | Unstressed | -0.280 | 0.039 | 0.8600000 | 0.1428286 | 51 | 0.8200000 | 0.1414214 | 50 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 32 | NO | 127 | Mating Duration | M | YES | Ambiguous | Unstressed | -0.371 | 0.032 | 534.6200000 | 204.1600000 | 64 | 466.0200000 | 160.0944118 | 63 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 32 | NO | 127 | Reproductive Success | F | NO | Direct | Unstressed | 0.156 | 0.031 | 34.2600000 | 18.7200000 | 64 | 37.1700000 | 18.2556840 | 63 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 32 | NO | 125 | Reproductive Success | M | NO | Direct | Unstressed | 0.315 | 0.032 | 0.6700000 | 0.3149603 | 62 | 0.7700000 | 0.3174902 | 63 | 4.259 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Blind | 1 | YES | 351 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.568 | 0.012 | 0.9400000 | 0.3200000 | 179 | 1.0800000 | 0.1300000 | 172 | 3.177 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Blind | 1 | YES | 336 | Immunity | M | NO | Ambiguous | Unstressed | 0.000 | 0.012 | 1.6500000 | 3.4000000 | 175 | 1.6500000 | 3.2000000 | 161 | 3.177 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Blind | 1 | YES | 413 | Immunity | F | NO | Ambiguous | Unstressed | -0.050 | 0.010 | 80.2000000 | 21.8000000 | 203 | 79.0500000 | 20.3000000 | 210 | 3.177 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Blind | 1 | YES | 788 | Immunity | B | NO | Ambiguous | Unstressed | -0.106 | 0.005 | NA | NA | NA | NA | NA | 401 | 3.177 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Blind | 1 | YES | 335 | Immunity | M | NO | Ambiguous | Unstressed | -0.108 | 0.012 | 0.5300000 | 0.1650000 | 173 | 0.5100000 | 0.2050000 | 162 | 3.177 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 0 | 1 | Blind | 1 | YES | 406 | Immunity | F | NO | Ambiguous | Unstressed | -0.098 | 0.010 | 0.5500000 | 0.2040000 | 202 | 0.5300000 | 0.2050000 | 204 | 3.177 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 47 | Mating Latency | M | YES | Indirect | Unstressed | 0.556 | 0.086 | 358.9000000 | 494.4000000 | 24 | 143.4000000 | 203.6000000 | 23 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 57 | Mating Latency | M | YES | Indirect | Unstressed | 0.470 | 0.070 | 294.7000000 | 313.6000000 | 28 | 158.0000000 | 259.1000000 | 29 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 53 | Mating Duration | M | YES | Ambiguous | Unstressed | 1.987 | 0.112 | 73.5000000 | 67.7000000 | 30 | 483.4000000 | 299.5000000 | 23 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 58 | Mating Duration | M | YES | Ambiguous | Unstressed | 0.551 | 0.070 | 181.8000000 | 198.5000000 | 29 | 323.3000000 | 298.6000000 | 29 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 53 | Mating Frequency | M | YES | Indirect | Unstressed | 1.982 | 0.112 | 2.1000000 | 2.2000000 | 30 | 22.2000000 | 15.0000000 | 23 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 58 | Mating Frequency | M | YES | Indirect | Unstressed | 0.929 | 0.075 | 4.2000000 | 4.1000000 | 29 | 15.0000000 | 15.7000000 | 29 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 30 | Reproductive Success | F | NO | Direct | Stressed | 1.852 | 0.183 | 183.8000000 | 80.6000000 | 15 | 409.5000000 | 147.0000000 | 15 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 30 | Reproductive Success | F | NO | Direct | Unstressed | 0.061 | 0.126 | 346.1000000 | 255.8000000 | 15 | 366.3000000 | 378.7000000 | 15 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.614 | 0.193 | 0.4570000 | 0.3580000 | 10 | 0.6320000 | 0.1450000 | 10 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.931 | 0.205 | 0.5290000 | 0.2500000 | 10 | 0.7200000 | 0.1210000 | 10 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.319 | 0.186 | 0.5700000 | 0.0640000 | 10 | 0.6210000 | 0.2070000 | 10 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | -0.219 | 0.156 | 0.4530000 | 0.3920000 | 12 | 0.3610000 | 0.4180000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.178 | 0.156 | 0.4450000 | 0.4240000 | 12 | 0.5140000 | 0.3180000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.025 | 0.155 | 0.4210000 | 0.3560000 | 12 | 0.4300000 | 0.3340000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.110 | 0.156 | 0.7970000 | 0.3520000 | 12 | 0.8330000 | 0.2730000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.724 | 0.166 | 0.6940000 | 0.3350000 | 12 | 0.9010000 | 0.2000000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | -0.389 | 0.159 | 0.8390000 | 0.2080000 | 12 | 0.7280000 | 0.3300000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 30 | Lifespan | F | NO | Indirect | Stressed | 0.211 | 0.127 | 8.3000000 | 2.5500000 | 15 | 8.9000000 | 2.9700000 | 15 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | High | High | High | 1 | 1 | Not Blind | 20 | NO | 29 | Lifespan | F | NO | Indirect | Unstressed | 0.677 | 0.138 | 8.8000000 | 2.7200000 | 14 | 10.3000000 | 1.4400000 | 15 | 5.146 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 27 | Mating Success | M | NO | Indirect | Unstressed | 0.657 | 0.076 | 0.2000000 | 0.1120000 | 27 | 0.2540000 | 0.1140000 | 27 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 29 | Mating Success | M | NO | Indirect | Unstressed | 0.041 | 0.069 | 0.8890000 | 0.0740000 | 28 | 0.8920000 | 0.0700000 | 30 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 31 | Mating Success | M | NO | Indirect | Unstressed | 0.211 | 0.063 | 0.8760000 | 0.1030000 | 31 | 0.9010000 | 0.1290000 | 31 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Latency | M | YES | Indirect | Unstressed | -0.062 | 0.014 | 2.9400000 | 1.8800000 | 149 | 3.1200000 | 3.6900000 | 143 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Duration | M | YES | Ambiguous | Unstressed | -0.918 | 0.015 | 11.7400000 | 2.3700000 | 149 | 14.0500000 | 2.6500000 | 143 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 122 | Mating Success | M | NO | Indirect | Unstressed | 0.153 | 0.016 | 0.0771000 | 0.1630000 | 122 | 0.1023000 | 0.1650000 | 121 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 50 | NO | 27 | Mating Success | M | NO | Indirect | Unstressed | 0.314 | 0.073 | 0.1700000 | 0.0720000 | 27 | 0.2000000 | 0.1120000 | 27 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 50 | NO | 29 | Mating Success | M | NO | Indirect | Unstressed | 0.613 | 0.073 | 0.8350000 | 0.0980000 | 28 | 0.8890000 | 0.0740000 | 28 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 50 | NO | 31 | Mating Success | M | NO | Indirect | Unstressed | -0.178 | 0.064 | 0.8970000 | 0.1290000 | 30 | 0.8760000 | 0.1030000 | 31 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Latency | M | YES | Indirect | Unstressed | 0.159 | 0.014 | 3.5600000 | 5.2200000 | 142 | 2.9400000 | 1.8800000 | 149 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Duration | M | YES | Ambiguous | Unstressed | 0.471 | 0.014 | 12.8800000 | 2.4600000 | 142 | 11.7400000 | 2.3700000 | 149 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 50 | NO | 122 | Mating Success | M | NO | Indirect | Unstressed | 0.170 | 0.016 | 0.0543000 | 0.0961000 | 122 | 0.0771000 | 0.1630000 | 122 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 27 | Mating Success | M | NO | Indirect | Unstressed | 0.868 | 0.079 | 0.1700000 | 0.0720000 | 27 | 0.2540000 | 0.1140000 | 27 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 29 | Mating Success | M | NO | Indirect | Unstressed | 0.660 | 0.073 | 0.8350000 | 0.0980000 | 28 | 0.8920000 | 0.0700000 | 30 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 31 | Mating Success | M | NO | Indirect | Unstressed | 0.031 | 0.064 | 0.8970000 | 0.1290000 | 30 | 0.9010000 | 0.1290000 | 31 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Latency | M | YES | Indirect | Unstressed | 0.097 | 0.014 | 3.5600000 | 5.2200000 | 142 | 3.1200000 | 3.6900000 | 143 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Duration | M | YES | Ambiguous | Unstressed | -0.456 | 0.014 | 12.8800000 | 2.4600000 | 142 | 14.0500000 | 2.6500000 | 143 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 122 | Mating Success | M | NO | Indirect | Unstressed | 0.355 | 0.017 | 0.0543000 | 0.0961000 | 122 | 0.1023000 | 0.1650000 | 121 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 1440 | Offspring Viability | B | NO | Direct | Unstressed | 0.088 | 0.001 | 0.8700000 | 0.3415260 | 1440 | 0.9000000 | 0.3415260 | 1440 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 50 | NO | 1440 | Offspring Viability | B | NO | Direct | Unstressed | -0.088 | 0.001 | 0.9000000 | 0.3415260 | 1440 | 0.8700000 | 0.3415260 | 1440 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Not Blind | 50 | NO | 1440 | Offspring Viability | B | NO | Direct | Unstressed | 0.000 | 0.001 | 0.9000000 | 0.3415260 | 1440 | 0.9000000 | 0.3415260 | 1440 | 4.659 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 27 | Body Size | F | YES | Ambiguous | Unstressed | -0.089 | 0.072 | 0.2826667 | 0.0124900 | 27 | 0.2822222 | 0.0101274 | 27 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Blind | 45 | NO | 27 | Body Size | F | YES | Ambiguous | Unstressed | -0.981 | 0.081 | 0.2943519 | 0.0103532 | 27 | 0.2826667 | 0.0124900 | 27 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 27 | Body Size | F | YES | Ambiguous | Unstressed | -1.183 | 0.085 | 0.2943519 | 0.0103532 | 27 | 0.2822222 | 0.0101274 | 27 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 30 | Mating Frequency | F | YES | Indirect | Unstressed | -0.090 | 0.065 | 6.7439524 | 3.1492291 | 30 | 7.0200794 | 2.9816484 | 30 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Blind | 45 | NO | 30 | Mating Frequency | F | YES | Indirect | Unstressed | 0.284 | 0.066 | 7.6940238 | 3.4353720 | 30 | 6.7439524 | 3.1492291 | 30 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 30 | Mating Frequency | F | YES | Indirect | Unstressed | 0.205 | 0.065 | 7.6940238 | 3.4353720 | 30 | 7.0200794 | 2.9816484 | 30 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 29 | Reproductive Success | F | NO | Direct | Unstressed | 0.185 | 0.067 | 50.3663793 | 7.6774347 | 29 | 51.5985906 | 5.1763110 | 29 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Blind | 45 | NO | 29 | Reproductive Success | F | NO | Direct | Unstressed | -0.890 | 0.075 | 56.1414116 | 4.7002918 | 28 | 50.3663793 | 7.6774347 | 29 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 29 | Reproductive Success | F | NO | Direct | Unstressed | -0.905 | 0.075 | 56.1414116 | 4.7002918 | 28 | 51.5985906 | 5.1763110 | 29 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 28 | Reproductive Success | F | NO | Direct | Stressed | 0.771 | 0.076 | 47.9508929 | 7.0792749 | 28 | 52.6177249 | 4.5419731 | 27 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Blind | 45 | NO | 28 | Reproductive Success | F | NO | Direct | Stressed | 0.168 | 0.067 | 42.6208333 | 8.3991535 | 30 | 47.9508929 | 7.0792749 | 28 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 28 | Reproductive Success | F | NO | Direct | Stressed | 1.439 | 0.087 | 42.6208333 | 8.3991535 | 30 | 52.6177249 | 4.5419731 | 27 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 29 | Lifespan | F | NO | Indirect | Unstressed | 1.315 | 0.081 | 33.4558333 | 4.1586802 | 30 | 38.8756979 | 3.9717452 | 29 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 27 | Lifespan | F | NO | Indirect | Unstressed | 0.189 | 0.074 | 33.2781463 | 4.5549330 | 28 | 33.4558333 | 4.1586802 | 30 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Blind | 45 | NO | 29 | Lifespan | F | NO | Indirect | Unstressed | 0.041 | 0.067 | 33.2781463 | 4.5549330 | 28 | 38.8756979 | 3.9717452 | 29 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | Low | Medium | Medium | 1 | 1 | Blind | 45 | NO | 27 | Lifespan | F | NO | Indirect | Unstressed | -0.669 | 0.078 | 56.2509143 | 5.8375189 | 25 | 57.2156463 | 4.2069037 | 28 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 29 | Lifespan | F | NO | Indirect | Unstressed | 1.295 | 0.083 | 60.1348639 | 5.1204446 | 28 | 56.2509143 | 5.8375189 | 25 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | Medium | Medium | High | 1 | 1 | Blind | 45 | NO | 27 | Lifespan | F | NO | Indirect | Unstressed | -0.612 | 0.073 | 60.1348639 | 5.1204446 | 28 | 57.2156463 | 4.2069037 | 28 | 4.612 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | Low | Medium | Low | 1 | 1 | Blind | 3 | YES | 20 | Body Size | M | YES | Ambiguous | Unstressed | -0.831 | 0.201 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | Low | Medium | Low | 1 | 1 | Blind | 3 | YES | 20 | Body Size | F | YES | Ambiguous | Unstressed | -0.831 | 0.201 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | Low | Medium | Low | 1 | 1 | Blind | 3 | YES | 20 | Male Attractiveness | M | NO | Indirect | Unstressed | 1.999 | 0.283 | 0.3850000 | 0.1090000 | 10 | 0.6210000 | 0.1170000 | 10 | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | Low | Medium | Low | 1 | 1 | Blind | 2 | YES | 100 | Reproductive Success | M | NO | Direct | Unstressed | 0.415 | 0.040 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | Low | Medium | Low | 1 | 1 | Blind | 2 | YES | 200 | Reproductive Success | F | NO | Direct | Unstressed | -0.118 | 0.020 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | Low | Medium | Low | 1 | 1 | Blind | 2 | YES | 12 | Reproductive Success | M | NO | Direct | Stressed | 0.835 | 0.313 | 4.5300000 | 3.5000000 | 6 | 9.5400000 | 7.0100000 | 6 | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | Low | Medium | Low | 1 | 1 | Blind | 2 | YES | 12 | Reproductive Success | M | NO | Direct | Unstressed | 0.849 | 0.314 | 13.5000000 | 11.5700000 | 6 | 23.1800000 | 9.3500000 | 6 | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | Low | Medium | Low | 1 | 1 | Blind | 2 | YES | 100 | Offspring Viability | M | NO | Direct | Unstressed | -0.304 | 0.041 | NA | NA | NA | NA | NA | NA | 3.407 |
| 53 | 23 | Partridge, L. | 1980 | Drosophila melanogaster | Fly | 100.000 | 1.00 | 200.00 | Low | High | Medium | 1 | 1 | Not Blind | 1 | YES | 41 | Offspring Viability | B | NO | Direct | Unstressed | 0.773 | 0.103 | 48.9000000 | 2.9495762 | 18 | 51.1000000 | 2.6645825 | 23 | NA |
| 53 | 23 | Partridge, L. | 1980 | Drosophila melanogaster | Fly | 100.000 | 1.00 | 200.00 | Low | High | Medium | 1 | 1 | Not Blind | 1 | YES | 35 | Offspring Viability | B | NO | Direct | Unstressed | 0.874 | 0.125 | 48.1000000 | 2.4083189 | 14 | 49.8000000 | 1.4832397 | 21 | NA |
| 53 | 23 | Partridge, L. | 1980 | Drosophila melanogaster | Fly | 100.000 | 1.00 | 200.00 | Low | High | Medium | 1 | 1 | Not Blind | 1 | YES | 60 | Offspring Viability | B | NO | Direct | Unstressed | 0.707 | 0.069 | 49.4400000 | 1.4142136 | 32 | 50.4500000 | 1.4142136 | 28 | NA |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 9 | YES | 171 | Body Size | F | YES | Ambiguous | Unstressed | 0.080 | 0.023 | 25.0000000 | 4.3826932 | 80 | 25.3600000 | 4.5789082 | 91 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 9 | YES | 284 | Body Size | M | YES | Ambiguous | Unstressed | 0.019 | 0.014 | 16.1800000 | 1.6099182 | 127 | 16.2100000 | 1.5982097 | 157 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 9 | YES | 284 | Male Attractiveness | M | NO | Indirect | Unstressed | 0.120 | 0.014 | 1.5900000 | 0.8624562 | 127 | 1.7000000 | 0.9589258 | 157 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 9 | YES | 284 | Male Attractiveness | M | NO | Indirect | Unstressed | 0.000 | 0.014 | 3.1300000 | 0.1437427 | 127 | 3.1300000 | 0.1278568 | 157 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 9 | YES | 284 | Male Attractiveness | M | NO | Indirect | Unstressed | 0.193 | 0.014 | 0.1600000 | 0.8624562 | 127 | 0.3300000 | 0.8949974 | 157 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 9 | YES | 284 | Male Attractiveness | M | NO | Indirect | Unstressed | 0.055 | 0.014 | 150.8900000 | 7.9058485 | 127 | 151.3400000 | 8.2900267 | 157 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 9 | YES | 174 | Reproductive Success | F | NO | Direct | Unstressed | -0.277 | 0.023 | 1.5900000 | 0.7244860 | 80 | 1.3820000 | 0.7659334 | 94 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 9 | YES | 173 | Offspring Viability | F | YES | Direct | Unstressed | 0.621 | 0.024 | 6.9400000 | 0.5992662 | 80 | 7.3200000 | 0.6171936 | 93 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 9 | YES | 145 | Offspring Viability | F | NO | Direct | Unstressed | 0.010 | 0.027 | 3.3200000 | 2.8195212 | 73 | 3.3500000 | 2.9698485 | 72 | 3.232 |
| 55 | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 84 | YES | 228 | Body Size | F | YES | Ambiguous | Unstressed | 0.973 | 0.020 | 0.8790000 | 0.0427083 | 114 | 0.9210000 | 0.0427083 | 114 | NA |
| 55 | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 84 | YES | 234 | Body Size | F | YES | Ambiguous | Unstressed | 0.763 | 0.018 | 0.8950000 | 0.0432666 | 117 | 0.9240000 | 0.0324500 | 117 | NA |
| 55 | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 84 | YES | 230 | Reproductive Success | F | NO | Direct | Unstressed | -0.363 | 0.018 | 129.1000000 | 80.4285397 | 115 | 99.0000000 | 84.7180618 | 115 | NA |
| 55 | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Not Blind | 84 | YES | 236 | Reproductive Success | F | NO | Direct | Unstressed | -0.246 | 0.017 | 122.0000000 | 86.9022439 | 118 | 101.2000000 | 81.4708537 | 118 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 61 | YES | 100 | Body Size | M | YES | Ambiguous | Unstressed | 2.115 | 0.062 | 233.1300000 | 16.9400000 | 50 | 270.8300000 | 18.4100000 | 50 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 61 | YES | 100 | Body Size | M | YES | Ambiguous | Unstressed | 1.346 | 0.048 | 211.6700000 | 19.8900000 | 50 | 237.1900000 | 17.6800000 | 50 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 61 | YES | 100 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 2.886 | 0.081 | 8.7307692 | 1.5410000 | 50 | 13.7564103 | 1.9037490 | 50 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 61 | YES | 100 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.596 | 0.041 | 7.7820513 | 2.2663679 | 50 | 9.0897436 | 2.0850585 | 50 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 61 | YES | 30 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 1.069 | 0.145 | 25.5723951 | 4.4651987 | 15 | 30.4600812 | 4.4023085 | 15 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 61 | YES | 30 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 1.484 | 0.163 | 27.4722598 | 3.3331765 | 15 | 32.9769959 | 3.8991876 | 15 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 81 | YES | 30 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.175 | 0.127 | 177.4228571 | 4.2492160 | 15 | 178.1600000 | 3.9836400 | 15 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 81 | YES | 30 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | -1.448 | 0.161 | 179.7885714 | 3.1869120 | 15 | 174.8857143 | 3.3860940 | 15 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 81 | YES | 178 | Mating Success | M | NO | Indirect | Unstressed | 0.015 | 0.022 | NA | NA | NA | NA | NA | NA | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 81 | YES | 180 | Mating Success | M | NO | Indirect | Unstressed | 0.148 | 0.022 | NA | NA | NA | NA | NA | NA | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 66 | YES | 140 | Reproductive Success | M | NO | Direct | Unstressed | -0.436 | 0.029 | NA | NA | NA | NA | NA | NA | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 38 | YES | 315 | Reproductive Success | M | NO | Direct | Unstressed | 0.022 | 0.014 | 0.5878581 | 0.3673826 | 112 | 0.5976808 | 0.4837226 | 203 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 1 | Blind | 38 | YES | 344 | Reproductive Success | M | NO | Direct | Unstressed | 0.327 | 0.012 | 0.4503411 | 0.4170165 | 162 | 0.5968622 | 0.4754863 | 182 | NA |
| 57 | 32 | Plesnar, A., M. Konior and J. Radwan | 2011 | Rhizoglyphus robini | Mite | 1.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 2 | NO | 80 | Offspring Viability | M | NO | Direct | Stressed | 0.060 | 0.049 | 0.7700000 | 0.1700000 | 40 | 0.7800000 | 0.1600000 | 40 | 1.029 |
| 57 | 32 | Plesnar, A., M. Konior and J. Radwan | 2011 | Rhizoglyphus robini | Mite | 1.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 2 | NO | 80 | Offspring Viability | M | NO | Direct | Unstressed | -0.094 | 0.049 | 0.9500000 | 0.1100000 | 40 | 0.9400000 | 0.1000000 | 40 | 1.029 |
| 58 | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 14 | YES | 60 | Reproductive Success | F | NO | Direct | Stressed | 1.504 | 0.127 | 31.0909091 | 15.1950949 | 11 | 92.8571429 | 43.4161068 | 49 | 5.683 |
| 58 | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 14 | YES | 95 | Reproductive Success | F | NO | Direct | Stressed | 1.171 | 0.071 | 134.5000000 | 48.3000374 | 48 | 143.1428571 | 49.8409939 | 56 | 5.683 |
| 58 | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 14 | YES | 104 | Reproductive Success | F | NO | Direct | Stressed | 0.174 | 0.038 | NA | NA | NA | NA | NA | NA | 5.683 |
| 58 | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 14 | YES | 117 | Reproductive Success | F | NO | Direct | Stressed | 0.526 | 0.120 | NA | NA | NA | NA | NA | NA | 5.683 |
| 58 | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | Low | Medium | Medium | 1 | 1 | Not Blind | 14 | YES | 11 | Extinction Rate | B | NO | Direct | Stressed | 1.510 | 0.740 | NA | NA | NA | NA | NA | NA | 5.683 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 5 | YES | 32 | Reproductive Success | F | NO | Direct | Stressed | 1.331 | 0.148 | 741.0000000 | 154.4321210 | 18 | 948.0000000 | 147.7954668 | 14 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 5 | YES | 32 | Reproductive Success | F | NO | Direct | Stressed | 1.339 | 0.149 | 37.0000000 | 7.6367532 | 18 | 47.4000000 | 7.4833148 | 14 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 5 | YES | 36 | Reproductive Success | F | NO | Direct | Unstressed | 1.242 | 0.128 | 602.0000000 | 143.8255193 | 18 | 752.0000000 | 84.8528137 | 18 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 5 | YES | 36 | Reproductive Success | F | NO | Direct | Unstressed | 1.240 | 0.128 | 30.1000000 | 7.2124892 | 18 | 37.6000000 | 4.2426407 | 18 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 5 | YES | 32 | Offspring Viability | F | NO | Direct | Stressed | 1.465 | 0.154 | 765.0000000 | 156.9777054 | 18 | 978.0000000 | 118.9847049 | 14 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 5 | YES | 32 | Offspring Viability | F | NO | Direct | Stressed | 1.428 | 0.153 | 38.3000000 | 8.0610173 | 18 | 48.9000000 | 5.9866518 | 14 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 5 | YES | 32 | Offspring Viability | B | NO | Direct | Stressed | 1.017 | 0.137 | 70.4000000 | 9.7580736 | 18 | 79.1000000 | 5.9866518 | 14 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 5 | YES | 36 | Offspring Viability | F | NO | Direct | Unstressed | 1.194 | 0.126 | 674.0000000 | 181.5850214 | 18 | 852.0000000 | 97.5807358 | 18 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 5 | YES | 36 | Offspring Viability | F | NO | Direct | Unstressed | 1.223 | 0.127 | 33.7000000 | 8.9095454 | 18 | 42.6000000 | 4.6669048 | 18 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | Low | Low | Low | 0 | 1 | Not Blind | 5 | YES | 36 | Offspring Viability | B | NO | Direct | Unstressed | 1.050 | 0.122 | 73.0000000 | 7.6367532 | 18 | 79.8000000 | 4.6669048 | 18 | 2.747 |
| 60 | 13 | Power, D. J. and L. Holman | 2015 | Callosobruchus maculatus | Beetle | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 0 | Blind | 3 | YES | 39 | Reproductive Success | F | NO | Direct | Unstressed | 0.160 | 0.099 | 0.6091667 | 0.1700941 | 20 | 0.5425014 | 0.1557092 | 19 | 2.747 |
| 60 | 13 | Power, D. J. and L. Holman | 2015 | Callosobruchus maculatus | Beetle | 2.000 | 3.00 | 4.00 | Medium | Low | Medium | 1 | 0 | Blind | 3 | YES | 39 | Offspring Viability | F | NO | Direct | Unstressed | -0.396 | 0.100 | 39.4500000 | 15.0559483 | 20 | 41.7368421 | 12.7446813 | 19 | 2.747 |
| 61 | 25 | Promislow, D. E. L., E. A. Smith and L. Pearse | 1998 | Drosophila melanogaster | Fly | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 13 | YES | 150 | Body Size | M | YES | Ambiguous | Unstressed | 0.100 | 0.026 | -0.0125000 | 0.2600000 | 75 | 0.0168000 | 0.3190000 | 75 | 9.821 |
| 61 | 25 | Promislow, D. E. L., E. A. Smith and L. Pearse | 1998 | Drosophila melanogaster | Fly | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 13 | YES | 150 | Body Size | F | YES | Ambiguous | Unstressed | -0.449 | 0.027 | 0.0950000 | 0.1750000 | 75 | -0.0870000 | 0.5430000 | 75 | 9.821 |
| 61 | 25 | Promislow, D. E. L., E. A. Smith and L. Pearse | 1998 | Drosophila melanogaster | Fly | 3.000 | 5.00 | 6.00 | High | Low | Medium | 1 | 1 | Not Blind | 17 | YES | 10182 | Offspring Viability | B | NO | Direct | Unstressed | 0.006 | 0.001 | NA | NA | NA | NA | NA | NA | 9.821 |
| 62 | 32 | Radwan, J. | 2004 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 2 | YES | 50 | Offspring Viability | B | NO | Direct | Stressed | 0.739 | 0.118 | 42.1100000 | 32.8700000 | 39 | 65.3900000 | 22.6900000 | 11 | 3.914 |
| 63 | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 11 | YES | 92 | Reproductive Success | F | NO | Direct | Unstressed | -0.142 | 0.043 | 112.7000000 | 25.1624442 | 46 | 108.7000000 | 30.3170150 | 46 | 2.893 |
| 63 | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 11 | YES | 66 | Reproductive Success | M | NO | Direct | Unstressed | -0.123 | 0.059 | 0.6170000 | 0.7180703 | 33 | 0.5430000 | 0.4423313 | 33 | 2.893 |
| 63 | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 11 | YES | 106 | Offspring Viability | B | NO | Direct | Unstressed | 0.106 | 0.037 | 0.7030000 | 0.1965630 | 53 | 0.7610000 | 0.7425712 | 53 | 2.893 |
| 63 | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 11 | YES | 90 | Lifespan | F | NO | Indirect | Unstressed | -0.085 | 0.044 | 25.3700000 | 21.1979244 | 45 | 23.7400000 | 16.4350996 | 45 | 2.893 |
| 64 | 34 | Rundle, H. D., S. F. Chenoweth and M. W. Blows | 2006 | Drosophila serrata | Fly | 55.000 | 1.00 | 110.00 | Low | High | Medium | 1 | 1 | Not Blind | 16 | YES | 552 | Reproductive Success | B | NO | Direct | Stressed | -0.067 | 0.007 | 30.4100000 | 40.5200000 | 276 | 27.6800000 | 40.5200000 | 276 | 4.292 |
| 64 | 34 | Rundle, H. D., S. F. Chenoweth and M. W. Blows | 2006 | Drosophila serrata | Fly | 55.000 | 1.00 | 110.00 | Low | High | Medium | 1 | 1 | Not Blind | 16 | YES | 552 | Reproductive Success | B | NO | Direct | Unstressed | -0.028 | 0.007 | 19.5700000 | 23.5600000 | 276 | 18.8300000 | 28.2700000 | 276 | 4.292 |
| 66 | 37 | Simmons, L. W. and F. Garcia-Gonzalez | 2008 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 20 | YES | 88 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.918 | 0.049 | 2.1300000 | 0.5969925 | 44 | 2.6000000 | 0.3979950 | 44 | 4.737 |
| 66 | 37 | Simmons, L. W. and F. Garcia-Gonzalez | 2008 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | Low | Medium | Low | 1 | 1 | Not Blind | 20 | YES | 88 | Body Condition | M | NO | Indirect | Unstressed | -0.727 | 0.048 | NA | NA | NA | NA | NA | NA | 4.737 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 37 | YES | 120 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.205 | 0.033 | 86.7000000 | 40.2790268 | 60 | 95.5000000 | 44.9266068 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 37 | YES | 120 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.259 | 0.033 | 90.7000000 | 52.6725735 | 60 | 102.4000000 | 35.6314468 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 37 | YES | 120 | Mating Success | M | NO | Indirect | Unstressed | 1.768 | 0.046 | 0.4310000 | 0.1006976 | 60 | 0.6170000 | 0.1084435 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 37 | YES | 120 | Mating Success | M | NO | Indirect | Unstressed | 0.282 | 0.033 | 0.4760000 | 0.5654556 | 60 | 0.6340000 | 0.5499636 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 37 | YES | 120 | Reproductive Success | F | NO | Direct | Unstressed | 0.022 | 0.033 | 284.3000000 | 105.3451470 | 60 | 286.8000000 | 120.8370804 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 37 | YES | 120 | Reproductive Success | F | NO | Direct | Unstressed | 0.123 | 0.033 | 278.0000000 | 61.1931369 | 60 | 284.4000000 | 39.5044301 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 37 | YES | 42 | Offspring Viability | F | NO | Direct | Unstressed | -0.287 | 0.093 | 97.8000000 | 0.4582576 | 21 | 97.5000000 | 1.3747727 | 21 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | Low | Medium | Low | 1 | 1 | Not Blind | 37 | YES | 42 | Offspring Viability | F | NO | Direct | Unstressed | -0.199 | 0.092 | 97.4000000 | 0.9165151 | 21 | 96.6000000 | 5.4990908 | 21 | 4.292 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 99 | Behavioural Plasticity | F | YES | Ambiguous | Unstressed | -0.018 | 0.040 | 285.4200000 | 196.1144075 | 50 | 282.2448980 | 155.8838417 | 49 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 98 | Behavioural Plasticity | F | YES | Ambiguous | Unstressed | -0.132 | 0.040 | 393.4166667 | 153.0849901 | 48 | 371.1800000 | 179.6165633 | 50 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 99 | Body Size | M | YES | Ambiguous | Unstressed | 0.155 | 0.040 | 3.4253300 | 0.5533225 | 50 | 3.5132898 | 0.4702925 | 49 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 98 | Body Size | F | YES | Ambiguous | Unstressed | 0.259 | 0.041 | 4.4623021 | 0.6760828 | 48 | 4.6295060 | 0.6208700 | 50 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 99 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.116 | 0.040 | 0.2007420 | 0.0585906 | 50 | 0.2075663 | 0.0648678 | 49 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 98 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | -0.022 | 0.040 | 0.1668542 | 0.0523804 | 48 | 0.1663250 | 0.0433648 | 50 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 99 | Mating Latency | M | YES | Indirect | Unstressed | 0.084 | 0.040 | 49.2000000 | 73.2039365 | 50 | 44.1836735 | 40.4874844 | 49 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 98 | Mating Latency | F | YES | Indirect | Unstressed | -0.105 | 0.040 | 69.6458333 | 86.1992964 | 48 | 61.4200000 | 68.2764758 | 50 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 96 | Immunity | M | NO | Ambiguous | Unstressed | -0.373 | 0.042 | 12.7920000 | 0.3350000 | 49 | 12.6780000 | 0.2580000 | 47 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 94 | Immunity | F | NO | Ambiguous | Unstressed | -0.564 | 0.044 | 12.9760000 | 0.2400000 | 47 | 12.8530000 | 0.1880000 | 47 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 99 | Mating Duration | M | YES | Ambiguous | Unstressed | 0.029 | 0.040 | 565.1000000 | 277.6167708 | 50 | 572.7551020 | 244.4586307 | 49 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | Low | High | Medium | 1 | 1 | Not Blind | 11 | NO | 98 | Mating Duration | F | YES | Ambiguous | Unstressed | 0.354 | 0.041 | 616.3958333 | 261.4579206 | 48 | 530.8400000 | 217.4206268 | 50 | 4.612 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 31 | NO | 180 | Mating Frequency | F | YES | Indirect | Unstressed | -0.236 | 0.011 | 0.3000000 | 0.5366563 | 180 | 0.6700000 | 2.1466253 | 180 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 33 | NO | 900 | Mating Frequency | M | YES | Indirect | Unstressed | 0.161 | 0.002 | 0.0390000 | 0.0900000 | 900 | 0.0650000 | 0.2100000 | 900 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 31 | NO | 180 | Mating Frequency | F | YES | Indirect | Unstressed | -0.178 | 0.011 | 0.2300000 | 0.1341641 | 180 | 0.3000000 | 0.5366563 | 180 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 33 | NO | 900 | Mating Frequency | M | YES | Indirect | Unstressed | -0.077 | 0.002 | 0.0530000 | 0.2400000 | 900 | 0.2300000 | 0.1341641 | 180 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 31 | NO | 180 | Mating Frequency | F | YES | Indirect | Unstressed | -0.288 | 0.011 | 0.2300000 | 0.1341641 | 180 | 0.6700000 | 2.1466253 | 180 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 33 | NO | 900 | Mating Frequency | M | YES | Indirect | Unstressed | 0.053 | 0.002 | 0.0530000 | 0.2400000 | 900 | 0.0650000 | 0.2100000 | 900 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.009 | 0.126 | 91.0000000 | 68.9050989 | 15 | 91.5000000 | 35.8880723 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.235 | 0.127 | 83.0000000 | 54.5498700 | 15 | 68.0000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.259 | 0.127 | 98.0000000 | 54.5498700 | 15 | 81.0000000 | 71.7761447 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.094 | 0.126 | 90.5000000 | 49.5255398 | 15 | 86.0000000 | 43.0656868 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.119 | 0.126 | 76.0000000 | 85.4136122 | 15 | 84.5000000 | 48.8077784 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.397 | 0.129 | 96.5000000 | 68.9050989 | 15 | 73.5000000 | 40.1946410 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.057 | 0.126 | 88.0000000 | 22.9683663 | 15 | 91.0000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.201 | 0.127 | 92.0000000 | 28.7104579 | 15 | 83.0000000 | 54.5498700 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.132 | 0.127 | 88.0000000 | 89.0024194 | 15 | 98.0000000 | 54.5498700 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.006 | 0.126 | 91.0000000 | 114.8418315 | 15 | 90.5000000 | 49.5255398 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.171 | 0.127 | 89.0000000 | 60.2919615 | 15 | 76.0000000 | 85.4136122 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.118 | 0.126 | 88.5000000 | 63.1630073 | 15 | 96.5000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.113 | 0.126 | 88.0000000 | 22.9683663 | 15 | 91.5000000 | 35.8880723 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.442 | 0.129 | 92.0000000 | 28.7104579 | 15 | 68.0000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.084 | 0.126 | 88.0000000 | 89.0024194 | 15 | 81.0000000 | 71.7761447 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.056 | 0.126 | 91.0000000 | 114.8418315 | 15 | 86.0000000 | 43.0656868 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.080 | 0.126 | 89.0000000 | 60.2919615 | 15 | 84.5000000 | 48.8077784 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.276 | 0.127 | 88.5000000 | 63.1630073 | 15 | 73.5000000 | 40.1946410 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.174 | 0.007 | 25.5400000 | 9.6994845 | 300 | 27.2600000 | 10.0458947 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.091 | 0.007 | 24.0500000 | 10.3923049 | 300 | 25.0300000 | 11.0851252 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.251 | 0.007 | 24.9300000 | 10.3923049 | 300 | 27.5900000 | 10.7387150 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | -0.390 | 0.007 | 42.3600000 | 16.8008928 | 300 | 36.3500000 | 13.8564065 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.485 | 0.007 | 33.3700000 | 18.5329436 | 300 | 43.3200000 | 22.3434554 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.227 | 0.007 | 38.4700000 | 23.2094808 | 300 | 43.8100000 | 23.7290961 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | 0.164 | 0.127 | 0.8200000 | 0.4880778 | 15 | 0.9000000 | 0.4593673 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.548 | 0.131 | 0.9100000 | 0.2583941 | 15 | 0.7200000 | 0.4019464 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.298 | 0.128 | 0.8800000 | 0.2583941 | 15 | 0.7600000 | 0.4880778 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.007 | 0.007 | 22.2400000 | 10.0458947 | 300 | 22.3100000 | 10.2190998 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | -0.251 | 0.007 | 23.9000000 | 11.9511506 | 300 | 21.1700000 | 9.6994845 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | -0.070 | 0.007 | 24.2800000 | 10.7387150 | 300 | 23.5500000 | 10.2190998 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.124 | 0.007 | 24.2700000 | 10.2190998 | 300 | 25.5400000 | 9.6994845 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.140 | 0.007 | 22.7300000 | 8.8334591 | 300 | 24.0500000 | 10.3923049 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.095 | 0.007 | 24.0100000 | 9.1798693 | 300 | 24.9300000 | 10.3923049 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.210 | 0.007 | 38.2400000 | 22.3434554 | 300 | 42.3600000 | 16.8008928 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | -0.457 | 0.007 | 41.2900000 | 16.1080725 | 300 | 33.3700000 | 18.5329436 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | -0.202 | 0.007 | 42.9300000 | 20.6114046 | 300 | 38.4700000 | 23.2094808 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.069 | 0.126 | 0.8600000 | 0.6316301 | 15 | 0.8200000 | 0.4880778 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | 0.040 | 0.126 | 0.9000000 | 0.2296837 | 15 | 0.9100000 | 0.2583941 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.058 | 0.126 | 0.9200000 | 0.9187347 | 15 | 0.8800000 | 0.2583941 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.061 | 0.007 | 21.6300000 | 10.0458947 | 300 | 22.2400000 | 10.0458947 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.159 | 0.007 | 22.1700000 | 9.6994845 | 300 | 23.9000000 | 11.9511506 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | Low | High | Medium | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.141 | 0.007 | 22.7800000 | 10.5655099 | 300 | 24.2800000 | 10.7387150 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.292 | 0.007 | 24.2700000 | 10.2190998 | 300 | 27.2600000 | 10.0458947 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.232 | 0.007 | 22.7300000 | 8.8334591 | 300 | 25.0300000 | 11.0851252 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.359 | 0.007 | 24.0100000 | 9.1798693 | 300 | 27.5900000 | 10.7387150 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | -0.100 | 0.007 | 38.2400000 | 22.3434554 | 300 | 36.3500000 | 13.8564065 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.104 | 0.007 | 41.2900000 | 16.1080725 | 300 | 43.3200000 | 22.3434554 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.041 | 0.007 | 42.9300000 | 20.6114046 | 300 | 43.8100000 | 23.7290961 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | 0.071 | 0.126 | 0.8600000 | 0.6316301 | 15 | 0.9000000 | 0.4593673 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.537 | 0.131 | 0.9000000 | 0.2296837 | 15 | 0.7200000 | 0.4019464 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.211 | 0.127 | 0.9200000 | 0.9187347 | 15 | 0.7600000 | 0.4880778 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.067 | 0.007 | 21.6300000 | 10.0458947 | 300 | 22.3100000 | 10.2190998 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | -0.103 | 0.007 | 22.1700000 | 9.6994845 | 300 | 21.1700000 | 9.6994845 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | Medium | High | High | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.074 | 0.007 | 22.7800000 | 10.5655099 | 300 | 23.5500000 | 10.2190998 | 300 | 3.719 |
Explanation of Dataframe:
Study ID: An ID given to the published paper the effect size is sourced from
Group ID: An ID given to the research group that may have published several papers on the same species usuing the same or very similar experimental setup
Species and Species ID: Same thing
SS Strength, Ratios and SS Density’s (Column 7-9): Various ratios of the number of males to females and the total number of individuals kept together in an experiment
Ratio Category: A three level category for the ratio of males to females (high, medium, low).
Density Category: A three level category for the density of males to females (high, medium, low).
SSS.Categorical: A three level category for the density & the ratio of males to females (high medium, low).
Post cop and Pre cop: Whether a study allowed Pre/Post-copulatory sexual selection (1) or not (0)
Blind: Whether the study was blind or not
Generations The number of generations run before fitness outcomes were measured
Enforced Monogamy: Whether the study had the low sexual selection treatment as enforced monogamy (YES) or not (NO). Not all studies compared enforced monogamy and SS+ treatments. Some used FB vs MB, where FB is the SS (low intensity).
Sex: Whether the fitness outcome was measured for females (F), males (M) or both (B). Studies that reported ‘both’ would pool results from males and females.
Ambiguous: Is the fitness outcome ambiguous (YES) or not ambigous (NO). Ambiguous outcomes may be those that may not necessarily be directional, that is to say they may be a life history trait.
Outcome Class: Grouped as Direct, Indirect or Ambiguous.
Environment: In the methods of the papers included in this study it was usually stated whether additional modifications to the experimental lines were made. Briefly, this was usually a modification that made conditions more stressful such as using a novel food source or elevated mutation load, the effect sizes from these experimental lines are labelled as ‘Stressed’. If it was clearly stated that there was no such modification it is labelled ‘Unstressed’. However, sometimes the paper was ambiguous in what lines had added stress or the results from stressed and unstressed lines were pooled together, in this case we label it as ‘Not Stated’.
g: Hedge’s g calculated using the compute.es package
var.g: The within study variance associated with the effect size, g
mean/sd/n.low/high: The means, standard deviation and sample size for the low or high sexual selection treatments, used to calculate lnCVR (meta-analysis of variance). Rows without these values had hedges g’ derived from summary statistics (F, z, chi-square etc.)
JIF: Journal Impact factor at year of publication. Several impact factors were unable to be determined/found and are NA.
Factor and level data
A lot of the covariates are categorical, so here we assign them as factors and relevel them.
prelim.data$Study.ID <- prelim.data$Study.ID %>% factor
prelim.data$Taxon <- prelim.data$Taxon %>% factor
prelim.data$Group.ID <- prelim.data$Group.ID %>% factor
prelim.data$Authors <- prelim.data$Authors %>% factor
prelim.data$Environment <- prelim.data$Environment %>% factor %>% relevel(ref="Unstressed")
prelim.data$Sex <- prelim.data$Sex %>% factor %>% relevel(ref="B")
prelim.data$Ambiguous <- prelim.data$Ambiguous %>% factor
prelim.data$Species <- prelim.data$Species %>% factor
#Outcome.Class.2 is using the categories that were decided by survey. I am keeping both just to check them against each other (how much of a difference it makes)
prelim.data$Outcome.Class <- prelim.data$Outcome.Class %>% factor %>% relevel(ref="Indirect")
prelim.data$Enforced.Monogamy <- prelim.data$Enforced.Monogamy %>% factor %>% relevel(ref="NO")
prelim.data$Pre.cop <- prelim.data$Pre.cop %>% factor %>% relevel(ref="0")
prelim.data$Post.cop <- prelim.data$Post.cop %>% factor %>% relevel(ref="0")
prelim.data$Ratio.Category <- prelim.data$Ratio.Category %>% factor %>% relevel(ref="Low")
prelim.data$Density.Category <- prelim.data$Density.Category %>% factor %>% relevel(ref="Low")
prelim.data$SSS.Categorical <- prelim.data$SSS.Categorical %>% factor %>% relevel(ref="Low")
prelim.data$Blinding <- prelim.data$Blinding %>% factorThe Effect Size Dataset
The number of effect sizes, publications, blind experiments, effect sizes in stressed conditions, male, female and both measures and different species used, with the number of effect sizes per taxon also reported.
n.blind.ones <- (sum(prelim.data$Blind == "Blind"))
n.stressed.ones <-
prelim.data %>% summarise(Effect_sizes = n(),
Publications = prelim.data$Study.ID %>% unique %>% length,
Blind_experiments = n.blind.ones,
Effect_sizes_.Stressedq = (sum(prelim.data$Environment == "Stressed")),
Effect_sizes_.Unstressedq = (sum(prelim.data$Environment == "Unstressed")),
Effect_sizes_.Maleq = (sum(prelim.data$Sex == "M")),
Effect_sizes_.Femaleq = (sum(prelim.data$Sex == "F")),
Effect_sizes_.Both_sexesq = (sum(prelim.data$Sex == "B")),
Different_species = prelim.data$Species %>% unique %>% length,
Effect_sizes_.Beetleq = sum(Taxon == "Beetle"),
Effect_sizes_.Flyq = sum(Taxon == "Fly"),
Effect_sizes_.Mouseq = sum(Taxon == "Mouse"),
Effect_sizes_.Nematodeq = sum(Taxon == "Nematode"),
Effect_sizes_.Miteq = sum(Taxon == "Mite"),
Effect_sizes_.Cricketq = sum(Taxon == "Cricket"),
Effect_sizes_.Guppyq = sum(Taxon == "Guppy")) %>% melt %>%
mutate(variable = gsub("_", " ", variable),
variable = gsub("[.]", "(", variable),
variable = gsub("q", ")", variable)) %>%
rename_("n" = "value", " " = "variable") %>% pander(split.cell = 40, split.table = Inf)No id variables; using all as measure variables
---------------------------------
n
--------------------------- -----
Effect sizes 459
Publications 65
Blind experiments 54
Effect sizes (Stressed) 94
Effect sizes (Unstressed) 335
Effect sizes (Male) 189
Effect sizes (Female) 219
Effect sizes (Both sexes) 51
Different species 15
Effect sizes (Beetle) 116
Effect sizes (Fly) 254
Effect sizes (Mouse) 40
Effect sizes (Nematode) 9
Effect sizes (Mite) 25
Effect sizes (Cricket) 6
Effect sizes (Guppy) 9
---------------------------------Forest plot using ggplot
Here we show the residual effect sizes for each outcome measured. The following forest plot is based on the residual effect sizes. The model is an intercept only model with standard random effects structure.
Although I am not sure whether having the raw values would be better. In addition whether it is worth it to add in summary polygons (effect sizes) for a grouped outcome
Using ggplot the forest plot is grouped according to environment and sex, with multiple effect sizes on one row for a given study
#Create standard random effectrs model
forest.model <- rma(g, var.g, mods = ~ 1, method = "REML", data = prelim.data)
#Obtain residuals
resstandards <- (rstandard.rma.uni(forest.model, type="response"))
#Create new df with residuals replacing raw
df.forest.model <- prelim.data
df.forest.model$g <- resstandards$resid
df.forest.model$sei <- resstandards$se
#Create new factor to order factors in a way where Ambig, Indirect and Direct are Grouped
df.forest.model$Outcome_f = factor(df.forest.model$Outcome, levels = c('Behavioural Plasticity', 'Body Size', 'Development Rate', 'Early Fecundity', 'Immunity', 'Mating Duration', 'Pesticide Resistance', 'Mutant Frequency', 'Body Condition', 'Fitness Senescence', 'Lifespan', 'Male Attractiveness', 'Mating Frequency', 'Mating Latency', 'Mating Success', 'Strength', 'Ejaculate Quality and Production', 'Extinction Rate', 'Offspring Viability', 'Reproductive Success'))
#define upper and lower bounds
df.forest.model$lowerci <- df.forest.model$g - 1.96*((df.forest.model$sei))
df.forest.model$upperci <- df.forest.model$g + 1.96*((df.forest.model$sei))
#Get author and year in one
df.forest.model$AuthorYear = paste(df.forest.model$Authors, df.forest.model$Year, sep=" ")
#Generate a plot
p.meta <- ggplot(df.forest.model, aes(y=reorder(AuthorYear, -g), x=g, xmin=lowerci, xmax=upperci, shape=Sex, color = Outcome.Class)) +
#Add data points and color them black
geom_point(size=2)+
geom_errorbarh(data=df.forest.model, aes(xmin= lowerci, xmax = upperci) ,height=.1)+
#Specify the limits of the x-axis and relabel it to something more meaningful
scale_x_continuous(limits=c(-5,5), name='Standardized Mean Difference (g)')+
#Give y-axis a meaningful label
ylab('Reference')+
#Add a vertical dashed line indicating an effect size of zero, for reference
geom_vline(xintercept=0, color='black', linetype='dashed')+
#Create sub-plots (i.e., facets) based on levels of setting
#And allow them to have their own unique axes (so authors don't redundantly repeat)
facet_grid(Outcome_f~., scales= 'free', space='free')+
theme(strip.text.y = element_text(angle = 0))
p.meta
Effect size of subgroups
The following documents how i could run individual models for each outcome or outcome.class of interest and then plot them. This may be good to add to the above forest plot (in the form of a polygon; how many studies report their estimates) but at the same time, it just adds results from different subsetted models rather than one combined one (as we use when investigating sex and environment)
There is not really enough direct outcomes to analyse by itself so hence we combine with indirect later
#Ambiguous outcomes
model.Ambiguous <- rma(g, var.g, mods = ~ 1, method = "REML", subset = (Outcome.Class == "Ambiguous"), intercept = T ,data = prelim.data)
summary(model.Ambiguous)
Random-Effects Model (k = 96; tau^2 estimator: REML)
logLik deviance AIC BIC AICc
-101.5560 203.1120 207.1120 212.2198 207.2425
tau^2 (estimated amount of total heterogeneity): 0.3901 (SE = 0.0637)
tau (square root of estimated tau^2 value): 0.6246
I^2 (total heterogeneity / total variability): 95.04%
H^2 (total variability / sampling variability): 20.18
Test for Heterogeneity:
Q(df = 95) = 1308.3434, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
0.0846 0.0679 1.2465 0.2126 -0.0484 0.2176
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #Indirect outcomes
model.Indirect <- rma(g, var.g, mods = ~ 1, method = "REML", subset = (Outcome.Class == "Indirect"), intercept = T ,data = prelim.data)
summary(model.Indirect)
Random-Effects Model (k = 147; tau^2 estimator: REML)
logLik deviance AIC BIC AICc
-151.2813 302.5625 306.5625 312.5297 306.6464
tau^2 (estimated amount of total heterogeneity): 0.3684 (SE = 0.0486)
tau (square root of estimated tau^2 value): 0.6069
I^2 (total heterogeneity / total variability): 96.61%
H^2 (total variability / sampling variability): 29.48
Test for Heterogeneity:
Q(df = 146) = 3576.9481, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
0.2490 0.0533 4.6680 <.0001 0.1445 0.3536 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #Direct outcomes
model.Direct <- rma(g, var.g, mods = ~ 1, method = "REML", subset = (Outcome.Class == "Direct"), data = prelim.data)
summary(model.Direct)
Random-Effects Model (k = 216; tau^2 estimator: REML)
logLik deviance AIC BIC AICc
-197.9389 395.8779 399.8779 406.6192 399.9345
tau^2 (estimated amount of total heterogeneity): 0.2463 (SE = 0.0303)
tau (square root of estimated tau^2 value): 0.4963
I^2 (total heterogeneity / total variability): 92.61%
H^2 (total variability / sampling variability): 13.53
Test for Heterogeneity:
Q(df = 215) = 1273.1096, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
0.1279 0.0385 3.3235 0.0009 0.0525 0.2033 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Now let’s combine into a data frame and plot. The following plot could then be added to the large forest plot as summary polygons or summary points with error bars.
#Data frame
Outcome.category <- c('Ambiguous', 'Indirect', 'Direct')
Estimate <- c(model.Ambiguous$b, model.Indirect$b, model.Direct$b)
l.ci <- c(model.Ambiguous$ci.lb, model.Indirect$ci.lb, model.Direct$ci.lb)
u.ci <- c(model.Ambiguous$ci.ub, model.Indirect$ci.ub, model.Direct$ci.ub)
data.1 <- data.frame(Outcome.category, Estimate, l.ci, u.ci)
#plot
data.1 %>% ggplot(aes(x = Estimate, y= Outcome.category)) +
geom_point() +
geom_vline(xintercept = 0, linetype = 2, colour = "grey70") +
geom_errorbarh(aes(xmin = l.ci, xmax = u.ci), height = 0, size=1) +
ylab("Outcome Category")+
xlab("Effect Size")+
xlim(-.2, .5)+
ggtitle('Meta-Analysis Results')
From the previous forest plot and models we see that overall sexual selection is beneficial towards population fitness. Although this is heavily modulated by the individual outcome and the outcome class. We explore heterogeneity in more depth later in this document.
Model predictions
Does the effect of sexual selection differ between species?
First run the model using a restricted dataset where we remove effect sizes with Ambiguous outcomes or environments that were not stated whether they were stressed or unstressed. In this model we use Sex, Environment, Taxon and the interaction between sex and environment as we hypothesise that the a stressful enviornment may be of greater importance to the female sex due to ‘female demographic dominance’, which essentially states that female fitness is more important to the overall population demographics and that most benefits or conversely costs will accrue to female fitness components.
restricted.data <- prelim.data %>%
filter(Outcome.Class != "Ambiguous" & Environment != "Not Stated") %>%
mutate(Sex = as.character(Sex), Environment = as.character(Environment), Outcome.Class.2 = as.character(Outcome.Class), Enforced.Monogamy = as.character(Enforced.Monogamy))
#RWe need to make sure the factors are leveled in the same order as we write our prediction function (below)
restricted.data$Environment <- restricted.data$Environment %>% factor %>% relevel(ref="Unstressed")
restricted.data$Sex <- restricted.data$Sex %>% factor %>% relevel(ref="M")
restricted.data$Outcome.Class <- restricted.data$Outcome.Class %>% factor %>% relevel(ref="Indirect")
#run model again with restricted data
model.complete <- rma.mv(g, var.g,
mods = ~ 1 + Sex + Environment + Taxon + Sex:Environment,
random = ~ 1 | Study.ID/Outcome,
method = "REML",
data = restricted.data)Redundant predictors dropped from the model.#Note that the data is subsetted
summary(model.complete)
Multivariate Meta-Analysis Model (k = 336; method: REML)
logLik Deviance AIC BIC AICc
-1201.7714 2403.5429 2429.5429 2478.7326 2430.7133
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0635 0.2519 56 no Study.ID
sigma^2.2 0.1528 0.3909 101 no Study.ID/Outcome
Test for Residual Heterogeneity:
QE(df = 325) = 4443.5982, p-val < .0001
Test of Moderators (coefficient(s) 2:11):
QM(df = 10) = 52.8349, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 0.3590 0.1151 3.1186 0.0018 0.1334 0.5846 **
SexB -0.0601 0.1136 -0.5297 0.5964 -0.2827 0.1624
SexF 0.0417 0.0341 1.2230 0.2213 -0.0251 0.1085
EnvironmentStressed -0.1151 0.0430 -2.6777 0.0074 -0.1993 -0.0308 **
TaxonCricket 0.2090 0.4914 0.4253 0.6706 -0.7542 1.1722
TaxonFly -0.2536 0.1375 -1.8453 0.0650 -0.5230 0.0158 .
TaxonGuppy -0.3292 0.3629 -0.9072 0.3643 -1.0406 0.3821
TaxonMite -0.1114 0.2022 -0.5510 0.5817 -0.5076 0.2849
TaxonMouse -0.3719 0.2087 -1.7815 0.0748 -0.7810 0.0372 .
SexB:EnvironmentStressed 0.1332 0.0920 1.4475 0.1478 -0.0472 0.3135
SexF:EnvironmentStressed 0.2528 0.0540 4.6806 <.0001 0.1469 0.3587 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 The result is a model with estimates for various taxa, species, sexes and environments. To make sense of these estimates we should obtain average predictions for each moderator variable class of interest. We can do that by using a modified version version of a function used by Holman 2017. Here it alows us to cluster predictions for the different moderators of interest: Sex, environment, taxon etc. This is done by obtaining predictions using the base predict() function for the rma.mv() objects that have been previously created
# function that makes predict.rma work like a normal predict() function, instead of the idiosyncratic way that it works by default.
get.predictions.complete <- function(newdata){
B<-0; F<-0; Stressed<-0; Cricket<-0; Fly<-0; Guppy<-0; Mite<-0; Mouse<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Unstressed<-1
if(newdata[3] == "Cricket") Cricket<-1
if(newdata[3] == "Fly") Fly<-1
if(newdata[3] == "Guppy") Guppy<-1
if(newdata[3] == "Mite") Mite<-1
if(newdata[3] == "Mouse") Mouse<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(model.complete, newmods=c(B, F, Stressed, Cricket, Fly, Guppy, Mite, Mouse, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.complete <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed"),
Taxon = c("Cricket", "Fly", "Guppy", "Mite", "Mouse")))
predictions.complete <- cbind(predictions.complete, do.call("rbind", apply(predictions.complete, 1, get.predictions.complete))) %>%
select(Sex, Environment, Taxon, pred, se, ci.lb, ci.ub)
for(i in 4:7) predictions.complete[,i] <- unlist(predictions.complete[,i])Note that to get vertical position dodge to use in the ggplot meta-analysis we need to utilise a vertical position dodge. We can obtain this formula from the following github page by Jared Lander: https://github.com/jaredlander/coefplot/blob/master/R/position.r
Third, plot the model predictions for effect size (Hedges’ g) for male, female and both sexes under both stressed and unstressed condition and faceted for each taxon.
I would liker to know how to join a table with mean, and CI values to the forest plots I am generating.
pd <- position_dodgev(height = .7)
predictions.complete %>% ggplot(aes(x = pred, y= Environment, colour = Sex)) +
geom_vline(xintercept = 0, linetype = 2, colour = "grey70") +
geom_errorbarh(aes(xmin = predictions.complete$ci.lb, xmax = predictions.complete$ci.ub), height = 0, position = pd) +
geom_point(position = pd, size=2) +
facet_grid(Taxon ~.)+
ylab("Environment")+
xlab("Model Prediction (Hedges g)")+
xlim(-2, 2)+
ggtitle('Effects of Sex, Stress and Outcome Class \non Population Fitness for Each Taxon')
This model indicates quite a bit of heterogeneity between taxon. More so that that confidence limits increase, rather than radically changing direction of effect. However we can se from this that under stressed environments, females twnd to
Does sexual selection benefit populations in stressed environments more than unstressed environments? AND Do the benefits of sexual selection accrue more for female fitness components?
Here we run a three level model where the outcome is nested within a study (Study.ID). Other potential random effects include Species and Group.ID. However the estimate (variance from random effect) of these two other potential random effects tended towards zero and were dropped from the model. Additionally the model could be run with just Study.ID, but from our exploration of heterogeneity (below) we see that there is sufficient correlation (but not ICC = 1) between outcomes to include it as a random effect within the model.
#run model without taxon: 3 level model with outcomes within a study
model.complete2 <- rma.mv(g, var.g,
mods = ~ 1 + Sex + Environment + Sex:Environment,
random = ~ 1 | Study.ID/Outcome,
method = "REML",
data = restricted.data)
summary(model.complete2)
Multivariate Meta-Analysis Model (k = 336; method: REML)
logLik Deviance AIC BIC AICc
-1207.9250 2415.8499 2431.8499 2462.2427 2432.2985
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0567 0.2381 56 no Study.ID
sigma^2.2 0.1592 0.3990 101 no Study.ID/Outcome
Test for Residual Heterogeneity:
QE(df = 330) = 4576.5205, p-val < .0001
Test of Moderators (coefficient(s) 2:6):
QM(df = 5) = 47.2343, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 0.1763 0.0605 2.9131 0.0036 0.0577 0.2949 **
SexB -0.0623 0.1132 -0.5503 0.5821 -0.2840 0.1595
SexF 0.0395 0.0341 1.1595 0.2463 -0.0273 0.1062
EnvironmentStressed -0.1195 0.0429 -2.7845 0.0054 -0.2036 -0.0354 **
SexB:EnvironmentStressed 0.1457 0.0919 1.5860 0.1127 -0.0344 0.3258
SexF:EnvironmentStressed 0.2566 0.0540 4.7530 <.0001 0.1508 0.3624 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #Generate predictions without taxon utilising the previously described function
get.predictions.complete2 <- function(newdata){
B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Unstressed<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(model.complete2, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.complete2 <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed")))
predictions.complete2 <- cbind(predictions.complete2, do.call("rbind", apply(predictions.complete2, 1, get.predictions.complete2))) %>%
select(Sex, Environment, pred, se, ci.lb, ci.ub)
for(i in 3:6) predictions.complete2[,i] <- unlist(predictions.complete2[,i])
#And plot the results
pd <- position_dodgev(height = .5)
predictions.complete2 %>% ggplot(aes(x = pred, y= Environment, colour = Sex)) +
geom_vline(xintercept = 0, linetype = 2, colour = "grey70") +
geom_errorbarh(aes(xmin = predictions.complete2$ci.lb, xmax = predictions.complete2$ci.ub), height = 0, position = pd) +
geom_point(position = pd) +
ylab("Environment")+
xlab("Effect Size (Hedges g)")+
xlim(-.75, .75)+
ggtitle('Effects of Sex, Stress \non Population Fitness')
We see that female fitness in stressed environments is greater than the other measurements. For outcomes that were measured for both female and males we see a greater uncertainty in the estimate. It is not obviously clear why this is. The ‘both’ outcomes are restricted to extinction rate, offspring viability, mutant frequency and reproductive success. However, the shift from ‘both’ being ns in unstressed to significant in stressed may reflect the dampening of the negative correlations (sexual antagonism).
table(restricted.data$Outcome, restricted.data$Sex)
M B F
Behavioural Plasticity 0 0 0
Body Condition 1 0 0
Body Size 0 0 0
Development Rate 0 0 0
Early Fecundity 0 0 0
Ejaculate Quality and Production 18 0 0
Extinction Rate 0 4 0
Fitness Senescence 3 0 3
Immunity 0 0 0
Lifespan 2 0 34
Male Attractiveness 6 0 0
Mating Duration 0 0 0
Mating Frequency 5 0 6
Mating Latency 12 0 1
Mating Success 35 0 0
Mutant Frequency 2 6 0
Offspring Viability 15 15 26
Pesticide Resistance 0 0 0
Reproductive Success 41 8 91
Strength 2 0 0Estimating Heterogeneity using I2 and exploring correlations
Let’s obtain a I2 statistic using the formulas presented here: http://www.metafor-project.org/doku.php/tips:i2_multilevel_multivariate
There are different methods to obtain estimates of I2, they should be pretty similar though. Here we obtain an overall value of I2 that is weighted based on variance where estimates of heterogeneity are sourced from sigma2 of the respective models.
#There are two estimates of heterogeneity: A between cluster (between studies) and a within-cluster (outcomes within a study)
#This is for the model with outcome within study
W <- diag(1/restricted.data$var.g)
X <- model.matrix(model.complete2)
P <- W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
100 * sum(model.complete2$sigma2) / (sum(model.complete2$sigma2) + (model.complete2$k-model.complete2$p)/sum(diag(P)))[1] 92.57563This is a reasonably high I2 value but is relatively common in Ecology and Evolution (Nakagawa 2017).
To investigate the sources of heterogeneity we can obtain a breakdown of the heterogeneity for the three level model.
100 * model.complete2$sigma2 / (sum(model.complete2$sigma2) + (model.complete2$k-model.complete2$p)/sum(diag(P)))[1] 24.30062 68.27501This indicates that 24.3 % of total heterogeneity is due to the between study heterogeneity and 68.275 % for within study heterogeneity between different outcomes. With the remaining 7.5 % due to sampling variance. Interestingly this might indicate that I2 would be largely reduced for a model restricted to a single outcome… Let’s test this with our most common outcome…Reproductive Success
#Reproductive Success Restriction
restricted.dataRS <- restricted.data %>% filter(restricted.data$Outcome == "Reproductive Success")
#Run reproductive success only model
model.completeRS <- rma.mv(g, var.g, mods = ~ 1 + Sex + Environment + Sex:Environment, random = ~ 1 | Study.ID, method = "REML", data = restricted.dataRS)
#Run estimate of heterogeneity
W2 <- diag(1/restricted.dataRS$var.g)
X2 <- model.matrix(model.completeRS)
P2 <- W2 - W2 %*% X2 %*% solve(t(X2) %*% W2 %*% X2) %*% t(X2) %*% W2
100 * sum(model.completeRS$sigma2) / (sum(model.completeRS$sigma2) + (model.completeRS$k-model.completeRS$p)/sum(diag(P2)))[1] 79.5345So if we look at an individual outcomes such as reproductive success our I^2 is lower (79.53 %). Which is still high as it comes from 39 studies but lower than others. If we wanted to run models independently we could do it for those with a large enough sample size (k>10).
Furthermore, we can obtain estimates of the intra-class correlation (ICC) within a study via:
round(model.complete2$sigma2[1] / sum(model.complete2$sigma2), 3)[1] 0.262This means that within a study, between different outcomes, there is a correlation of 26.2 % (low-medium). This justifies including outcome as a level, as without it we would be assuming ICC = 1. We can also gain an estimate of the total heterogeniety, as the sum of the sigma componenets:
round(sum(model.complete2$sigma2), 3)[1] 0.216Multilevel model using metafors alternative random effect structure
This inner, outer factor stuff from the metafor package is a bit strange. There is a description in
?rma.mv()but still unsure how it differs from the above model. It seems that it is useful to breakdown variance-covariance matrix but unsure how that would benefit our analysis.
#Just to check, how about with outcome as the inner factor
model.complete2.2 <- rma.mv(g, var.g, mods = ~ 1 + Sex + Environment + Sex:Environment, random = ~ factor(Outcome) | Study.ID, method = "REML", data = restricted.data)
summary(model.complete2.2)
Multivariate Meta-Analysis Model (k = 336; method: REML)
logLik Deviance AIC BIC AICc
-1207.9250 2415.8499 2431.8499 2462.2427 2432.2985
Variance Components:
outer factor: Study.ID (nlvls = 56)
inner factor: factor(Outcome) (nlvls = 13)
estim sqrt fixed
tau^2 0.2159 0.4647 no
rho 0.2626 no
Test for Residual Heterogeneity:
QE(df = 330) = 4576.5205, p-val < .0001
Test of Moderators (coefficient(s) 2:6):
QM(df = 5) = 47.2344, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 0.1763 0.0605 2.9130 0.0036 0.0577 0.2949 **
SexB -0.0623 0.1132 -0.5503 0.5821 -0.2840 0.1595
SexF 0.0395 0.0341 1.1595 0.2463 -0.0273 0.1062
EnvironmentStressed -0.1195 0.0429 -2.7845 0.0054 -0.2036 -0.0354 **
SexB:EnvironmentStressed 0.1457 0.0919 1.5860 0.1127 -0.0344 0.3258
SexF:EnvironmentStressed 0.2566 0.0540 4.7530 <.0001 0.1508 0.3624 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #Now with a slightly different structure (HCS)
model.complete2.3 <- rma.mv(g, var.g, mods = ~ 1 + Sex + Environment + Sex:Environment, random = ~ Outcome | Study.ID, struct = "HCS", method = "REML", data = restricted.data)
summary(model.complete2.3)
Multivariate Meta-Analysis Model (k = 336; method: REML)
logLik Deviance AIC BIC AICc
-1196.2648 2392.5295 2432.5295 2508.5114 2435.2480
Variance Components:
outer factor: Study.ID (nlvls = 56)
inner factor: Outcome (nlvls = 13)
estim sqrt k.lvl fixed level
tau^2.1 2.3154 1.5217 1 no Body Condition
tau^2.2 0.4694 0.6851 18 no Ejaculate Quality and Production
tau^2.3 0.3083 0.5553 4 no Extinction Rate
tau^2.4 0.0075 0.0865 6 no Fitness Senescence
tau^2.5 0.1011 0.3179 36 no Lifespan
tau^2.6 2.4494 1.5650 6 no Male Attractiveness
tau^2.7 0.5773 0.7598 11 no Mating Frequency
tau^2.8 0.2323 0.4820 13 no Mating Latency
tau^2.9 0.8082 0.8990 35 no Mating Success
tau^2.10 0.0729 0.2700 8 no Mutant Frequency
tau^2.11 0.1205 0.3471 56 no Offspring Viability
tau^2.12 0.1762 0.4197 140 no Reproductive Success
tau^2.13 0.0158 0.1257 2 no Strength
rho 0.6386 no
Test for Residual Heterogeneity:
QE(df = 330) = 4576.5205, p-val < .0001
Test of Moderators (coefficient(s) 2:6):
QM(df = 5) = 45.8143, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 0.1122 0.0498 2.2543 0.0242 0.0147 0.2098 *
SexB -0.0198 0.0952 -0.2075 0.8356 -0.2064 0.1669
SexF 0.0407 0.0338 1.2058 0.2279 -0.0255 0.1069
EnvironmentStressed -0.1095 0.0429 -2.5538 0.0107 -0.1935 -0.0255 *
SexB:EnvironmentStressed 0.1262 0.0889 1.4193 0.1558 -0.0481 0.3005
SexF:EnvironmentStressed 0.2494 0.0537 4.6410 <.0001 0.1441 0.3548 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #Generate predictions without taxon
get.predictions.complete2 <- function(newdata){
B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Unstressed<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(model.complete2.2, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.complete2 <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed")))
predictions.complete2 <- cbind(predictions.complete2, do.call("rbind", apply(predictions.complete2, 1, get.predictions.complete2))) %>%
select(Sex, Environment, pred, se, ci.lb, ci.ub)
for(i in 3:6) predictions.complete2[,i] <- unlist(predictions.complete2[,i])
#And plot the results
pd <- position_dodgev(height = .3)
predictions.complete2 %>% ggplot(aes(x = pred, y= Environment, colour = Sex)) +
geom_vline(xintercept = 0, linetype = 2, colour = "grey70") +
geom_errorbarh(aes(xmin = predictions.complete2$ci.lb, xmax = predictions.complete2$ci.ub), height = 0, position = pd) +
geom_point(position = pd) +
ylab("Environment")+
xlab("Effect Size (Hedges g)")+
xlim(-.75, .75)+
ggtitle('Effects of Sex, Stress \non Population Fitness (OUTCOME = INNER FACTOR)')
Meta-Analysis on Variance
This meta-analysis on variation utilises previously described and utilised methods devoleped (Nakagawa et al. 2015; Senior et al. 2016). Our goal is to determine whether the phenotypic variance in fitness related traits is impacted by sexual selection. We would assume that if selection is occuring not only would the trait mean shift in a certain direction but the variance associated with those changes to the mean would also decrease. In this case we use an effect size statistic known as the natural log of the coefficient of variation ratio (lnCVR)
First, we setup our calculation by creating a a restricted dataset with only unabmiguous fitness outcomes and running the functions developed by Nakagawa et al. 2015:
#Setup restricted data
prelim.data2 <- (prelim.data %>% filter(Outcome.Class != "Ambiguous"))
#Run function for lnCVR and associated variance of lnCVR
#for lnCVR
Calc.lnCVR<-function(CMean, CSD, CN, EMean, ESD, EN){
ES<-log(ESD) - log(EMean) + 1 / (2*(EN - 1)) - (log(CSD) - log(CMean) + 1 / (2*(CN - 1)))
return(ES)
}
#for variance of lnCVR
Calc.var.lnCVR<-function(CMean, CSD, CN, EMean, ESD, EN, Equal.E.C.Corr=T){
if(Equal.E.C.Corr==T){
mvcorr<-cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate
S2<- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * mvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * mvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
}
else{
Cmvcorr<-cor.test(log(CMean), log(CSD))$estimate
Emvcorr<-cor.test(log(EMean), (ESD))$estimate
S2<- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * Cmvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * Emvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
}
return(S2)
}Secondly, we utilise those formulas to obtain lnCVR and var.CVR for all applicable effect sizes. Noting that not all of the dataset has means, SD and n; some were calculated from summary statistics and are not able to have lnCVR calculated. Once we obtain these lnCVR estimates we can run subsetted models that use as the response variable:
Now utilise function with existing data frame
#foe lnCVR
prelim.data2$lnCVr <- Calc.lnCVR(prelim.data2$mean.low, prelim.data2$sd.low, prelim.data2$n.low, prelim.data2$mean.high, prelim.data2$sd.high, prelim.data2$n.high)
#for variance in lnCVR
prelim.data2$var.lnCVr <- Calc.var.lnCVR(prelim.data2$mean.low, prelim.data2$sd.low, prelim.data2$n.low, prelim.data2$mean.high, prelim.data2$sd.high, prelim.data2$n.high, Equal.E.C.Corr=F)
#Run simple models subsetted for each environment/sex (this is perhaps a clunky way so we also use predictions shown below)
# For stressed environment and females
varSF <- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~ 1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Stressed" & Sex == "F"))Rows with NAs omitted from model fitting.# For stressed environment and females
varSM <- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~ 1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Stressed" & Sex == "M"))Rows with NAs omitted from model fitting.# For stressed environment and females
varSB <- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~ 1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Stressed" & Sex == "B"))Rows with NAs omitted from model fitting.#For Benign environment and females
varUF<- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Unstressed" & Sex == "F"))Rows with NAs omitted from model fitting.#For Benign environment and males
varUM<- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Unstressed" & Sex == "M"))Rows with NAs omitted from model fitting.#For Benign environment and both
varUB <- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Unstressed" & Sex == "B"))Rows with NAs omitted from model fitting.#Create dataframe of estimates and confidence intervals
lnCVR <- c(varSF$b, varSM$b, varSB$b, varUF$b, varUM$b, varUB$b)
l.ci <- c(varSF$ci.lb, varSM$ci.lb, varSB$ci.lb, varUF$ci.lb, varUM$ci.lb, varUB$ci.lb)
u.ci <- c(varSF$ci.ub, varSM$ci.ub, varSB$ci.ub, varUF$ci.ub, varUM$ci.ub, varUB$ci.ub)
Environment <- c("Stressed", "Stressed", "Stressed", "Unstressed", "Unstressed", "Unstressed")
Sex <- c("Female", "Male", "Both", "Female", "Male", "Both")
k <- c(varSF$k, varSM$k, varSB$k, varUF$k, varUM$k, varUB$k)
var.data <- data.frame(lnCVR, l.ci, u.ci, Environment, Sex, k)
#Releveling the factors to make sure it aligns with other formatted graphs
var.data$Environment <- var.data$Environment %>% factor %>% relevel(ref="Unstressed")
var.data$Sex <- var.data$Sex %>% factor %>% relevel(ref="Male")
#Plot subseted model estimates
var.data %>% ggplot(aes(x=lnCVR, y = Environment, colour = Sex))+
geom_vline(xintercept = 0, linetype = 2, colour = "grey70") +
geom_errorbarh(aes(xmin = l.ci, xmax = u.ci), height = 0, position = pd) +
geom_point(position = pd) +
ylab("Environment")+
xlab("lnCVR")+
xlim(-.75, .75)+
ggtitle('Meta-Analysis of Variance (Using Subsetting)')
Here we see that Stressed environments tend to reduce phenotypic variance (if anything). However, perhaps a better way to conduct this analysis is not through subsetting but through utilising model predictions as we did with Hedges’ g previously. This can be done be utilising the same predict function but for lnCVR and var.lnCVR.
Multilevel-model using lnCVR:
#Now try with multilevel model
variance.model <- rma.mv(lnCVr, var.lnCVr, mods = ~ 1 + Sex + Environment + Sex:Environment, random = ~ 1 | Study.ID/Outcome, method = "REML", data = prelim.data2 %>% filter(Environment != "Not Stated"))Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.summary(variance.model)
Multivariate Meta-Analysis Model (k = 277; method: REML)
logLik Deviance AIC BIC AICc
-3871.9266 7743.8533 7759.8533 7788.6702 7760.4029
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.1080 0.3287 46 no Study.ID
sigma^2.2 0.3488 0.5906 80 no Study.ID/Outcome
Test for Residual Heterogeneity:
QE(df = 271) = 13217.4543, p-val < .0001
Test of Moderators (coefficient(s) 2:6):
QM(df = 5) = 4835.2829, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt -0.0612 0.1578 -0.3878 0.6982 -0.3706 0.2482
SexF -0.1357 0.1523 -0.8910 0.3730 -0.4342 0.1628
SexM 0.5825 0.1532 3.8014 0.0001 0.2822 0.8829 ***
EnvironmentStressed -0.5085 0.0639 -7.9550 <.0001 -0.6338 -0.3832 ***
SexF:EnvironmentStressed -0.6235 0.0685 -9.1004 <.0001 -0.7578 -0.4892 ***
SexM:EnvironmentStressed 0.0333 0.0700 0.4754 0.6345 -0.1039 0.1705
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Plotted predictions of lnCVR for various moderators:
#Generate predictions
get.predictions.variance <- function(newdata){
F<-0; M<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "F") F<-1
if(newdata[1] == "M") M<-1
if(newdata[2] == "Stressed") Unstressed<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "M" & newdata[2] == "Stressed") interaction2<-1
predict(variance.model, newmods=c(F, M, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.variance <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed")))
predictions.variance <- cbind(predictions.variance, do.call("rbind", apply(predictions.variance, 1, get.predictions.variance))) %>%
select(Sex, Environment, pred, se, ci.lb, ci.ub)
for(i in 3:6) predictions.variance[,i] <- unlist(predictions.variance[,i])
#And plot the results
pd <- position_dodgev(height = .3)
predictions.variance %>% ggplot(aes(x = pred, y= Environment, colour = Sex)) +
geom_vline(xintercept = 0, linetype = 2, colour = "grey70") +
geom_errorbarh(aes(xmin = predictions.variance$ci.lb, xmax = predictions.variance$ci.ub), height = 0, position = pd) +
geom_point(position = pd) +
ylab("Environment")+
xlab("lnCVR")+
xlim(-1.2, 1.2)+
ggtitle('Meta-Analysis of Variance (Using Model Predictions)')
From these predictions we see that environmental stress has a large impact on phenotypic variance, whereby phenotypic variance is lower for females than for males and under stressful conditions the phenotypic variance of females decreases. Indicating there is a narrowing of phenotypic variance under selection.
Bias
Funnel plots indicate potential but small amounts of publication bias
Checking for biases with a funnell plot. Note that the trim and fill method does not work with rma.mv objects. However we can perform Eggers test using the ragtest() function. This tests for asymmetry via assessing relationships between effect size and a specified predictor. See ?ragtest() for more information. Also the eggers test does not work for rma.mv objects.
trim.ambig <- trimfill.rma.uni(model.Ambiguous)
funnel.rma(trim.ambig)
regtest(model.Ambiguous, predictor = "vi")
Regression Test for Funnel Plot Asymmetry
model: mixed-effects meta-regression model
predictor: sampling variance
test for funnel plot asymmetry: z = 1.6784, p = 0.0933funnel.rma(model.Indirect)
regtest(model.Indirect, predictor = "vi")
Regression Test for Funnel Plot Asymmetry
model: mixed-effects meta-regression model
predictor: sampling variance
test for funnel plot asymmetry: z = 6.1203, p < .0001funnel.rma(model.Direct)
regtest(model.Direct, predictor = "vi")
Regression Test for Funnel Plot Asymmetry
model: mixed-effects meta-regression model
predictor: sampling variance
test for funnel plot asymmetry: z = 3.1078, p = 0.0019funnel.rma(model.complete2, type="rstandard", yaxis = "sei")
Now use ggplot for funnel plot. This is pretty clunky and unlike the
funnel.rmait does not use residuals but raw effect sizes, we could of course add residuals, so it depends on how much customization you think these plots should have. The outline taken from: https://sakaluk.wordpress.com/2016/02/16/7-make-it-pretty-plots-for-meta-analysis/
#Funnel plot for indirect model
#Make restricted data for indirect
prelim.data.Indirect <- prelim.data %>% filter(Outcome.Class == "Indirect")
#read in apatheme
#My APA-format theme
apatheme=theme_bw()+
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
panel.border=element_blank(),
axis.line=element_line(),
text=element_text(family='Times'),
legend.position='none')
#Store the meta-analytic estimate and its standard error from whatever model you run (substitute your own values)
estimate = model.Indirect$b
se = model.Indirect$se
#Store a vector of values that spans the range from 0
#to the max value of impression (standard error) in your dataset.
#Make the increment (the final value) small enough (I choose 0.001)
#to ensure your whole range of data is captured
se.seq=seq(0, max(sqrt(prelim.data.Indirect$var.g)), 0.001)
#Now, compute vectors of the lower-limit and upper limit values for
#the 95% CI region, using the range of SE that you generated in the previous step, and the stored value of your meta-analytic estimate.
ll95 = estimate-(1.96*se.seq)
ul95 = estimate+(1.96*se.seq)
#You can do this for a 99% CI region too
ll99 = estimate-(3.29*se.seq)
ul99 = estimate+(3.29*se.seq)
#And finally, do the same thing except now calculating the confidence interval
#for your meta-analytic estimate based on the stored value of its standard error
meanll95 = estimate-(1.96*se)
meanul95 = estimate+(1.96*se)
#Now, smash all of those calculated values into one data frame (called 'dfCI').
#You might get a warning about '...row names were found from a short variable...'
#You can ignore it.
dfCI = data.frame(ll95, ul95, ll99, ul99, se.seq, estimate, meanll95, meanul95)row names were found from a short variable and have been discarded
#Now we can actually make the funnel plot.
#Using your original data-frame, map standard error to your x-axis (for now) and Zr to your y-axis
fp = ggplot(prelim.data.Indirect, aes_string(x = sqrt(prelim.data.Indirect$var.g), y =prelim.data.Indirect$g)) +
#Add your data-points to the scatterplot
geom_point(shape = 1) +
#Give the x- and y- axes informative labels
xlab('Standard Error') + ylab('g')+
#Now using the 'dfCI' data-frame we created, plot dotted lines corresponding
#to the lower and upper limits of your 95% CI region,
#And dashed lines corresponding to your 99% CI region
geom_line(aes(x = se.seq, y = ll95), linetype = 'dotted', data = dfCI) +
geom_line(aes(x = se.seq, y = ul95), linetype = 'dotted', data = dfCI) +
geom_line(aes(x = se.seq, y = ll99), linetype = 'dashed', data = dfCI) +
geom_line(aes(x = se.seq, y = ul99), linetype = 'dashed', data = dfCI) +
#Now plot dotted lines corresponding to the 95% CI of your meta-analytic estimate
geom_segment(aes(x = min(se.seq), y = meanll95, xend = max(se.seq), yend = meanll95), linetype='dotted', data=dfCI) +
geom_segment(aes(x = min(se.seq), y = meanul95, xend = max(se.seq), yend = meanul95), linetype='dotted', data=dfCI) +
#Reverse the x-axis ordering (se) so that the tip of the funnel will appear
#at the top of the figure once we swap the x- and y-axes...
scale_x_reverse()+
#Specify the range and interval for the tick-marks of the y-axis (Zr);
#Choose values that work for you based on your data
scale_y_continuous(breaks=seq(-1.25,2,0.25))+
#And now we flip the axes so that SE is on y- and Zr is on x-
coord_flip()+
#Finally, apply my APA-format theme (see code at end of post).
#You could, alternatively, specify theme_bw() instead.
apatheme
#Call the pretty funnel plot
fp
Other tests of publication bias suggest there is little evidence for publication bias
Journal Impact Factor
If we see a positive trend with effect size and Journal Impact Factor it may represent publication bias whereby significant (positive) results are published more readily and in more circulated journals. Our journal impact factor dataset is not evenly distributed as several publications in Nature (JIF ~ 40) are much larger than the next highest JIF (~11).
prelim.data %>% ggplot(aes(x=JIF, y=(g)))+
geom_jitter(color='darkgreen', alpha=.3, aes(size = (1/(var.g))))+
geom_hline(yintercept=0, linetype = 'dotted')+
geom_smooth(method='lm', color='black')+
labs(size = 'weight')
I think it may also be interesting to plot the maximum effect size for each study and then plot the results as many studies measure a suite of traits.
Time-lag Bias
We can also look at the time-lag bias, which suggests effect size decreases over time. Again, because one publication from 1980 is well before the next publication in the late 1990s we see a very uneven distribution.
prelim.data %>% ggplot(aes(x=Year, y=g))+
geom_jitter(color='orange', alpha=.3, aes(size = (1/(var.g))))+
geom_hline(yintercept=0, linetype = 'dotted')+
geom_smooth(method='lm', color='black')+
labs(size = 'weight')
Blinding
From these graphs, we see a small trend for larger effect sizes to be published in higher impact journals as well as for effect size to decrease over successive years. Additionally to publication bias, other forms of bias may exist within studies. We initially collected data on whether studies were blind or not. Although not enough studies were blind for us to include it as a fixed effect within the model we can test whether blinding affects the raw effect size:
df.forest.model %>% ggplot(aes(x=Blinding, y=g))+
geom_boxplot()+
geom_jitter(aes(color=Blinding))+
geom_hline(yintercept=0, linetype = 'dotted')
Sample Size
We also collected sample sizes for each of the effect sizes calculated. Because we are dealing withj different taxa some studies are not suited to have sample sizes in the 1000’s. We can simply inspect the sample size ande effect sizes through the following plot:
prelim.data %>% ggplot(aes(x=(n), y = g))+
geom_point()+
xlim(0,2100)+
facet_grid(Taxon~., scales='free')+
ylim(-3.5,3.5)+
geom_hline(yintercept=0, linetype="dashed")
(Promislow 1998 has one sample size of >10,000) and is not shown here. From these plots we can see that with increased sample size the effect sizes are closer to zero. Thistrend should be taken into account as meta-analytic models are wighted by 1/variance.
Generations
We recorded the number of generations of experimental exolution each study used. The number of generations proved a negligable predictor in the meta-analytic models and can be seen here:
prelim.data %>% ggplot(aes(x=Generations, y=g, color=Taxon))+
geom_point()+
ylim(-3.5,3.5)+
geom_hline(yintercept=0, linetype="dashed")
---
title: "Effects of sexual selection on non-sexual fitness components: a meta-analysis"
subtitle: Supplementary Material
author: "Justin Cally, University of Melbourne"
output: html_notebook
---

```{r warning=FALSE, message=FALSE}
library(knitr)
library(pander)
library(compute.es)
library(metafor)
library(dplyr)
library(lme4)
library(forestplot)
library(ggplot2)
library(MuMIn)
library(glmulti)
library(cowplot)
library(ggrepel)
library(reshape2)
```



# Methods

Our aim was to investigate the effects of sexual selection on population fitness by conducting a meta-analysis on studies that measured fitness related outcomes after experimentally evolving a population under varying levels of opportunity for sexual selection. Here we describe the process of the literature search, data extraction, effect size calculation, formulation of multilevel models and assessing publication bias.

##Literature Search (Extended methods from paper)

**The literature search was conducted under the following conditions:**

1. We searched ISI Web of Science and Scopus on 9th June 2017. Notably, these resulted in a different set of returns (**PRISMA Figure**).

2. Studies were restricted to those from peer-reviewed and in the English language.

3. We devised a search strategy that sought to find studies which manipulated the presence or strength of sexual selection using experimental evolution, and then measured some proxy of population fitness. As such the search terms were as follows: 


####_ISI Web of Science_


Topic (TS) = “Sexual Selection” OR Promisc* OR Monogam* OR Polygam* OR Polyandr* OR Polygyn* OR “Mate choice”

AND

Topic (TS) = Fitness OR “Population Fitness” OR Deleterious OR “Male Strength” OR Fecund* OR Viability OR Productiv* OR “Reproductive Success” OR “Reproductive Rate” OR Surviv* OR | “Development Rate” OR Extinct* OR “Competitive Success” OR Mortality OR Mass OR “Body Size” OR “Wing Size” OR Emergence OR Mating Rate OR “Mating Propensity” OR Adapt* OR “Novel | Environment” OR “Sexual Conflict” OR “Sexual Antagonis*”

AND

Topic (TS) = Generations OR “Experimental evolution” OR “mutation load”

AND

Research Area (SU) = “Evolutionary Biology”

####_Scopus_


TITLE-ABS-KEY = “Sexual Selection” OR Promisc* OR Monogam* OR Polygam* OR Polyandr* OR Polygyn* OR “Mate choice”

AND

TITLE-ABS-KEY = Fitness OR “Population Fitness” OR Deleterious OR “Male Strength” OR Fecund* OR Viability OR Productiv* OR “Reproductive Success” OR “Reproductive Rate” OR Surviv* | OR “Development Rate” OR Extinct* OR “Competitive Success” OR Mortality OR Mass OR “Body Size” OR “Wing Size” OR Emergence OR Mating Rate OR “Mating Propensity” OR Adapt* OR | “Novel Environment” OR “Sexual Conflict” OR “Sexual Antagonis*”

AND

TITLE-ABS-KEY = Generations OR “Experimental evolution” OR “mutation load”


In addition to studies found from the literature search we also included three relevant studies that were identified during scoping but not picked up in the subsequent formal searches (Partridge 1980; Price et al. 2010; Savic Veselinovic et al. 2013) that were missed by the database searches (**PRISMA Figure**). 

4. After removing duplicates, we read the titles and abstracts of the remaining 1015 papers, and removed papers that were not relevant (typically because they were not an empirical study using experimental evolution). This left 130 papers, for which we read the full text and applied the following selection criteria: 

  + **(1: Study Design)** The study was an experimental evolution study lasting >1 generation
  + **(1: Population)** a) The study was conducted using an animal species that was b) diecious
  + **(1: Intervention and Control)** The study experimentally manipulated the strength of sexual selection (e.g. via enforced monogamy or an altered sex ratio)
  + **(1: Outcomes)** The study measured a trait that we judged to be a potential correlate of population fitness. 
  
This latter criterion is likely to be contentious, because there is rarely enough data justify the assumption that a particular trait is (or is not) correlated with population fitness. We therefore relied on our best judgement when deciding which studies to exclude (see Table XXX). The inclusion/exlusion critera as applied to each study are detailed in an accompaning spreadsheet.

```{r}
Eligibility.criteria <- read.csv('Eligibility Workbook(22.02).csv', fileEncoding="UTF-8")
Eligibility.criteria %>% pander(split.cell = 20, split.table = 250, style = "grid")
```



##Data Extraction

The raw extracted data table is presented in the accompanying data folder. It details the type of data collected for each study (arithmatic means, SD, n, F-statistic, chi-squared, proportion etc.). The rules utilised were as follows: 

1. Arithmatic means, standard deviations/errors and sample sizes were extracted from a paper, supplementary material or a linked data repository (e.g. Data Dryad). This was possible when means and SD were reported in text or in a table. We would preferentially extract data for each experimental evolution line/replicat/family if possible and only extract data for the final reported generation (which was noted down).

2. If we could not find the means and SD in text format we used web-plot digitizer (v.3.12) to extract data from graphs. 

3. If means were not reported then we ecxtracted a summary statistic or proportion value, which we could later convert to Hedges g' using the _compute.es_ package. Summary statistics included _F_, _z_, _t_ and _chi^2^_. These conversions still required providing sample sizes for each treatment so these needed to be extractable from the study. Some summary statistics were obtained from generalized linear model summary tabels, others from straight forward ANOVAs and then some from more complex analysis such as proportional hazards statistical tests. 

4. The covariates collected were extensive (**DATA TABLE**) and are discussed later. They were all regarded as potentially explaining trends in effect size or bias and hence were collected.


##Effect Size Calculation

> Not sure what to do about these calculations. I did not really lay them out neatly and the compute.es does not really return neat values. My calculations were done in a clunkier manual way. And are shown below.


**SS- will be group one with increased SS being group two**


####Almbro and Simmons 2014**


######Brood Number (No Distinction) 
```{r}
mes(15.4, 18, 10.071, 10.071, 207, 207, dig = 3)
```

######Strength (Stressed) 
```{r}
mes(0.047, 0.094, 0.05724, 0.16217, 91, 91, dig = 3)
```


##The Meta-Analysis Dataset


### Setup

Read in csv file 
```{r}
prelim.data <- read.csv('Preliminary data frame 22.2.18.csv')
kable(prelim.data)
```

###Explanation of Dataframe: 

**Study ID**: An ID given to the published paper the effect size is sourced from

**Group ID**: An ID given to the research group that may have published several papers on the same species usuing the same or very similar experimental setup

**Species and Species ID**: Same thing

**SS Strength, Ratios and SS Density's (Column 7-9)**: Various ratios of the number of males to females and the total number of individuals kept together in an experiment

**Ratio Category**: A three level category for the ratio of males to females (high, medium, low).

**Density Category**: A three level category for the density of males to females (high, medium, low).

**SSS.Categorical**: A three level category for the **density & the ratio** of males to females (high medium, low). 

**Post cop and Pre cop**: Whether a study allowed Pre/Post-copulatory sexual selection (1) or not (0)

**Blind**: Whether the study was blind or not

**Generations** The number of generations run before fitness outcomes were measured

**Enforced Monogamy**: Whether the study had the low sexual selection treatment as enforced monogamy (YES) or not (NO). Not all studies compared enforced monogamy and SS+ treatments. Some used FB vs MB, where FB is the SS (low intensity). 

**Sex**: Whether the fitness outcome was measured for females (F), males (M) or both (B). Studies that reported 'both' would pool results from males and females.

**Ambiguous**: Is the fitness outcome ambiguous (YES) or not ambigous (NO). Ambiguous outcomes may be those that may not necessarily be directional, that is to say they may be a life history trait. 

**Outcome Class**: Grouped as Direct, Indirect or Ambiguous.

**Environment**: In the methods of the papers included in this study it was usually stated whether additional modifications to the experimental lines were made. Briefly, this was usually a modification that made conditions more stressful such as using a novel food source or elevated mutation load, the effect sizes from these experimental lines are labelled as 'Stressed'. If it was clearly stated that there was no such modification it is labelled 'Unstressed'. However, sometimes the paper was ambiguous in what lines had added stress or the results from stressed and unstressed lines were pooled together, in this case we label it as 'Not Stated'. 

**g**: Hedge's g calculated using the compute.es package

**var.g**: The within study variance associated with the effect size, g

**mean/sd/n.low/high**: The means, standard deviation and sample size for the low or high sexual selection treatments, used to calculate lnCVR (meta-analysis of variance). Rows without these values had hedges g' derived from summary statistics (F, z, chi-square etc.)

**JIF**: Journal Impact factor at year of publication. Several impact factors were unable to be determined/found and are NA.

_______________________________________________

### Factor and level data

A lot of the covariates are categorical, so here we assign them as factors and relevel them.

```{r}
prelim.data$Study.ID <- prelim.data$Study.ID %>% factor
prelim.data$Taxon <- prelim.data$Taxon %>% factor
prelim.data$Group.ID <- prelim.data$Group.ID %>% factor 
prelim.data$Authors <- prelim.data$Authors %>% factor
prelim.data$Environment <- prelim.data$Environment %>% factor %>% relevel(ref="Unstressed")
prelim.data$Sex <- prelim.data$Sex %>% factor %>% relevel(ref="B")
prelim.data$Ambiguous <- prelim.data$Ambiguous %>% factor
prelim.data$Species <- prelim.data$Species %>% factor
#Outcome.Class.2 is using the categories that were decided by survey. I am keeping both just to check them against each other (how much of a difference it makes)
prelim.data$Outcome.Class <- prelim.data$Outcome.Class %>% factor %>% relevel(ref="Indirect")
prelim.data$Enforced.Monogamy <- prelim.data$Enforced.Monogamy %>% factor %>% relevel(ref="NO")
prelim.data$Pre.cop <- prelim.data$Pre.cop %>% factor %>% relevel(ref="0")
prelim.data$Post.cop <- prelim.data$Post.cop %>% factor %>% relevel(ref="0")
prelim.data$Ratio.Category <- prelim.data$Ratio.Category %>% factor %>% relevel(ref="Low")
prelim.data$Density.Category <- prelim.data$Density.Category %>% factor %>% relevel(ref="Low")
prelim.data$SSS.Categorical <- prelim.data$SSS.Categorical %>% factor %>% relevel(ref="Low")
prelim.data$Blinding <- prelim.data$Blinding %>% factor
```

________________________________________________

###The Effect Size Dataset

The number of effect sizes, publications, blind experiments, effect sizes in stressed conditions, male, female and both measures and different species used, with the number of effect sizes per taxon also reported. 

```{r}
n.blind.ones <- (sum(prelim.data$Blind == "Blind"))
n.stressed.ones <- 
prelim.data %>% summarise(Effect_sizes = n(), 
                       Publications = prelim.data$Study.ID %>% unique %>% length,
                       Blind_experiments = n.blind.ones,
                       Effect_sizes_.Stressedq = (sum(prelim.data$Environment == "Stressed")),
                       Effect_sizes_.Unstressedq = (sum(prelim.data$Environment == "Unstressed")),         
                       Effect_sizes_.Maleq = (sum(prelim.data$Sex == "M")),
                       Effect_sizes_.Femaleq = (sum(prelim.data$Sex == "F")),
                       Effect_sizes_.Both_sexesq = (sum(prelim.data$Sex == "B")),
                       Different_species =  prelim.data$Species %>% unique %>% length,
                       Effect_sizes_.Beetleq = sum(Taxon == "Beetle"),
                       Effect_sizes_.Flyq = sum(Taxon == "Fly"),
                       Effect_sizes_.Mouseq = sum(Taxon == "Mouse"),
                       Effect_sizes_.Nematodeq = sum(Taxon == "Nematode"),
                       Effect_sizes_.Miteq = sum(Taxon == "Mite"),
                       Effect_sizes_.Cricketq = sum(Taxon == "Cricket"),
                       Effect_sizes_.Guppyq = sum(Taxon == "Guppy")) %>% melt %>%
  mutate(variable = gsub("_", " ", variable),
         variable = gsub("[.]", "(", variable),
         variable = gsub("q", ")", variable)) %>% 
  rename_("n" = "value", " " = "variable") %>% pander(split.cell = 40, split.table = Inf)
```



###Forest plot using ggplot 

Here we show the residual effect sizes for each outcome measured. The following forest plot is based on the residual effect sizes. The model is an intercept only model with standard random effects structure.

> Although I am not sure whether having the raw values would be better. In addition whether it is worth it to add in summary polygons (effect sizes) for a grouped outcome

Using ggplot the forest plot is grouped according to environment and sex, with multiple effect sizes on one row for a given study

```{r, fig.height=30, fig.width=22.5}
#Create standard random effectrs model
forest.model <- rma(g, var.g, mods = ~ 1, method = "REML", data = prelim.data)

#Obtain residuals
resstandards <- (rstandard.rma.uni(forest.model, type="response"))

#Create new df with residuals replacing raw
df.forest.model <- prelim.data
df.forest.model$g <- resstandards$resid
df.forest.model$sei <- resstandards$se

#Create new factor to order factors in a way where Ambig, Indirect and Direct are Grouped
df.forest.model$Outcome_f = factor(df.forest.model$Outcome, levels = c('Behavioural Plasticity', 'Body Size', 'Development Rate', 'Early Fecundity', 'Immunity', 'Mating Duration', 'Pesticide Resistance', 'Mutant Frequency', 'Body Condition', 'Fitness Senescence', 'Lifespan', 'Male Attractiveness', 'Mating Frequency', 'Mating Latency', 'Mating Success', 'Strength', 'Ejaculate Quality and Production', 'Extinction Rate', 'Offspring Viability', 'Reproductive Success'))

#define upper and lower bounds
df.forest.model$lowerci <- df.forest.model$g - 1.96*((df.forest.model$sei))
df.forest.model$upperci <- df.forest.model$g + 1.96*((df.forest.model$sei))

#Get author and year in one
df.forest.model$AuthorYear = paste(df.forest.model$Authors, df.forest.model$Year, sep=" ")


#Generate a plot
p.meta <- ggplot(df.forest.model, aes(y=reorder(AuthorYear, -g), x=g, xmin=lowerci, xmax=upperci, shape=Sex, color = Outcome.Class)) +
 #Add data points and color them black
geom_point(size=2)+
geom_errorbarh(data=df.forest.model, aes(xmin= lowerci, xmax = upperci) ,height=.1)+
  #Specify the limits of the x-axis and relabel it to something more meaningful
  scale_x_continuous(limits=c(-5,5), name='Standardized Mean Difference (g)')+
  #Give y-axis a meaningful label
  ylab('Reference')+
  #Add a vertical dashed line indicating an effect size of zero, for reference
  geom_vline(xintercept=0, color='black', linetype='dashed')+
  #Create sub-plots (i.e., facets) based on levels of setting
  #And allow them to have their own unique axes (so authors don't redundantly repeat)
  facet_grid(Outcome_f~., scales= 'free', space='free')+
  theme(strip.text.y = element_text(angle = 0))
p.meta
```

___________________
###Effect size of subgroups 

>The following documents how i could run individual models for each outcome or outcome.class of interest and then plot them. This may be good to add to the above forest plot (in the form of a polygon; how many studies report their estimates) but at the same time, it just adds results from different subsetted models rather than one combined one (as we use when investigating sex and environment)

There is not really enough direct outcomes to analyse by itself so hence we combine with indirect later
```{r}
#Ambiguous outcomes

model.Ambiguous <- rma(g, var.g, mods = ~ 1, method = "REML", subset = (Outcome.Class == "Ambiguous"), intercept = T ,data = prelim.data)

summary(model.Ambiguous)
```

```{r}
#Indirect outcomes

model.Indirect <- rma(g, var.g, mods = ~ 1, method = "REML", subset = (Outcome.Class == "Indirect"), intercept = T ,data = prelim.data)

summary(model.Indirect)
```

```{r}
#Direct outcomes

model.Direct <- rma(g, var.g, mods = ~ 1, method = "REML", subset = (Outcome.Class == "Direct"), data = prelim.data)

summary(model.Direct)
```

Now let's combine into a data frame and plot. The following plot could then be added to the large forest plot as summary polygons or summary points with error bars. 

```{r}
#Data frame
Outcome.category <- c('Ambiguous', 'Indirect', 'Direct')
Estimate <- c(model.Ambiguous$b, model.Indirect$b, model.Direct$b)
l.ci <- c(model.Ambiguous$ci.lb, model.Indirect$ci.lb, model.Direct$ci.lb)
u.ci <- c(model.Ambiguous$ci.ub, model.Indirect$ci.ub, model.Direct$ci.ub)
data.1 <- data.frame(Outcome.category, Estimate, l.ci, u.ci)

#plot

data.1 %>% ggplot(aes(x = Estimate, y= Outcome.category)) + 
  geom_point() +
  geom_vline(xintercept = 0, linetype = 2, colour = "grey70") + 
  geom_errorbarh(aes(xmin = l.ci, xmax = u.ci), height = 0, size=1) +
  ylab("Outcome Category")+
  xlab("Effect Size")+
  xlim(-.2, .5)+
  ggtitle('Meta-Analysis Results')
```

From the previous forest plot and models we see that overall sexual selection is beneficial towards population fitness. Although this is heavily modulated by the individual outcome and the outcome class. We explore heterogeneity in more depth later in this document. 

__________________________________

###Model predictions


####Does the effect of sexual selection differ between species?



First run the model using a restricted dataset where we remove effect sizes with Ambiguous outcomes or environments that were not stated whether they were stressed or unstressed. In this model we use Sex, Environment, Taxon and the interaction between sex and environment as we hypothesise that the a stressful enviornment may be of greater importance to the female sex due to 'female demographic dominance', which essentially states that female fitness is more important to the overall population demographics and that most benefits or conversely costs will accrue to female fitness components. 

```{r}
restricted.data <- prelim.data %>% 
  filter(Outcome.Class != "Ambiguous" & Environment != "Not Stated") %>% 
  mutate(Sex = as.character(Sex), Environment = as.character(Environment), Outcome.Class.2 = as.character(Outcome.Class), Enforced.Monogamy = as.character(Enforced.Monogamy))

#RWe need to make sure the factors are leveled in the same order as we write our prediction function (below)
restricted.data$Environment <- restricted.data$Environment %>% factor %>% relevel(ref="Unstressed")
restricted.data$Sex <- restricted.data$Sex %>% factor %>% relevel(ref="M")
restricted.data$Outcome.Class <- restricted.data$Outcome.Class %>% factor %>% relevel(ref="Indirect")

#run model again with restricted data 
model.complete <- rma.mv(g, var.g, 
                         mods = ~ 1 + Sex + Environment + Taxon + Sex:Environment, 
                         random = ~ 1 | Study.ID/Outcome, 
                         method = "REML", 
                         data = restricted.data)

#Note that the data is subsetted
summary(model.complete) 

```

The result is a model with estimates for various taxa, species, sexes and environments. To make sense of these estimates we should obtain average predictions for each moderator variable class of interest. We can do that by using a modified version version of a function used by Holman 2017. Here it alows us to cluster predictions for the different moderators of interest: Sex, environment, taxon etc. This is done by obtaining predictions using the base ``predict()`` function for the ``rma.mv()`` objects that have been previously created
```{r}
# function that makes predict.rma work like a normal predict() function, instead of the idiosyncratic way that it works by default.
get.predictions.complete <- function(newdata){
  B<-0; F<-0; Stressed<-0; Cricket<-0; Fly<-0; Guppy<-0; Mite<-0; Mouse<-0; interaction1<-0; interaction2<-0; interaction3<-0
  if(newdata[1] == "B") B<-1 
  if(newdata[1] == "F") F<-1 
  if(newdata[2] == "Stressed") Unstressed<-1
  if(newdata[3] == "Cricket") Cricket<-1
  if(newdata[3] == "Fly") Fly<-1
  if(newdata[3] == "Guppy") Guppy<-1
  if(newdata[3] == "Mite") Mite<-1
  if(newdata[3] == "Mouse") Mouse<-1
  if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
  if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1

  predict(model.complete, newmods=c(B, F, Stressed, Cricket, Fly, Guppy, Mite, Mouse, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.complete <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
                           Environment = c("Unstressed", "Stressed"),
                           Taxon = c("Cricket", "Fly", "Guppy", "Mite", "Mouse")))
predictions.complete <- cbind(predictions.complete, do.call("rbind", apply(predictions.complete, 1, get.predictions.complete))) %>%
  select(Sex, Environment, Taxon, pred, se, ci.lb, ci.ub) 
for(i in 4:7) predictions.complete[,i] <- unlist(predictions.complete[,i])
```

Note that to get vertical position dodge to use in the ggplot meta-analysis we need to utilise a vertical position dodge. We can obtain this formula from the following github page by Jared Lander: https://github.com/jaredlander/coefplot/blob/master/R/position.r
```{r include=FALSE}
# Detect and prevent collisions.
# Powers dodging, stacking and filling.
collidev <- function(data, height = NULL, name, strategy, check.height = TRUE) {
    # Determine height
    if (!is.null(height)) {
        # height set manually
        if (!(all(c("ymin", "ymax") %in% names(data)))) {
            data$ymin <- data$y - height / 2
            data$ymax <- data$y + height / 2
        }
    } else {
        if (!(all(c("ymin", "ymax") %in% names(data)))) {
            data$ymin <- data$y
            data$ymax <- data$y
        }
        
        # height determined from data, must be floating point constant
        heights <- unique(data$ymax - data$ymin)
        heights <- heights[!is.na(heights)]
        
        #   # Suppress warning message since it's not reliable
        #     if (!zero_range(range(heights))) {
        #       warning(name, " requires constant height: output may be incorrect",
        #         call. = FALSE)
        #     }
        height <- heights[1]
    }
    
    # Reorder by x position, relying on stable sort to preserve existing
    # ordering, which may be by group or order.
    data <- data[order(data$ymin), ]
    
    # Check for overlap
    intervals <- as.numeric(t(unique(data[c("ymin", "ymax")])))
    intervals <- intervals[!is.na(intervals)]
    
    if (length(unique(intervals)) > 1 & any(diff(scale(intervals)) < -1e-6)) {
        warning(name, " requires non-overlapping y intervals", call. = FALSE)
        # This is where the algorithm from [L. Wilkinson. Dot plots.
        # The American Statistician, 1999.] should be used
    }
    
    if (!is.null(data$xmax)) {
        plyr::ddply(data, "ymin", strategy, height = height)
    } else if (!is.null(data$x)) {
        data$xmax <- data$x
        data <- plyr::ddply(data, "ymin", strategy, height = height)
        data$x <- data$xmax
        data
    } else {
        stop("Neither x nor xmax defined")
    }
}

# Stack overlapping intervals.
# Assumes that each set has the same horizontal position
pos_stackv <- function(df, height) {
    if (nrow(df) == 1) return(df)
    
    n <- nrow(df) + 1
    x <- ifelse(is.na(df$x), 0, df$x)
    if (all(is.na(df$y))) {
        heights <- rep(NA, n)
    } else {
        heights <- c(0, cumsum(x))
    }
    
    df$xmin <- heights[-n]
    df$xmax <- heights[-1]
    df$x <- df$xmax
    df
}

# Stack overlapping intervals and set height to 1.
# Assumes that each set has the same horizontal position.
pos_fillv <- function(df, height) {
    stacked <- pos_stackv(df, height)
    stacked$xmin <- stacked$xmin / max(stacked$xmax)
    stacked$xmax <- stacked$xmax / max(stacked$xmax)
    stacked$x <- stacked$xmax
    stacked
}

# Dodge overlapping interval.
# Assumes that each set has the same horizontal position.
pos_dodgev <- function(df, height) {
    n <- length(unique(df$group))
    if (n == 1) return(df)
    
    if (!all(c("ymin", "ymax") %in% names(df))) {
        df$ymin <- df$y
        df$ymax <- df$y
    }
    
    d_height <- max(df$ymax - df$ymin)
    
    # df <- data.frame(n = c(2:5, 10, 26), div = c(4, 3, 2.666666,  2.5, 2.2, 2.1))
    # ggplot(df, aes(n, div)) + geom_point()
    
    # Have a new group index from 1 to number of groups.
    # This might be needed if the group numbers in this set don't include all of 1:n
    groupidy <- match(df$group, sort(unique(df$group)))
    
    # Find the center for each group, then use that to calculate xmin and xmax
    df$y <- df$y + height * ((groupidy - 0.5) / n - .5)
    df$ymin <- df$y - d_height / n / 2
    df$ymax <- df$y + d_height / n / 2
    
    df
}


#' Adjust position by dodging overlaps to the side.
#'
#' @inheritParams ggplot2::position_identity
#' @param height Dodging height, when different to the height of the individual
#'   elements. This is useful when you want to align narrow geoms with wider
#'   geoms. See the examples for a use case.
#' @family position adjustments
#' @export
#' @examples
#' ggplot(mtcars, aes(factor(cyl), fill = factor(vs))) +
#'   geom_bar(position = "dodge")
#'
#' ggplot(diamonds, aes(price, fill = cut)) +
#'   geom_histogram(position="dodge")
#' # see ?geom_boxplot and ?geom_bar for more examples
#'
#' # To dodge items with different heights, you need to be explicit
#' df <- data.frame(x=c("a","a","b","b"), y=2:5, g = rep(1:2, 2))
#' p <- ggplot(df, aes(x, y, group = g)) +
#'   geom_bar(
#'     stat = "identity", position = "dodge",
#'     fill = "grey50", colour = "black"
#'   )
#' p
#'
#' # A line range has no height:
#' p + geom_linerange(aes(ymin = y-1, ymax = y+1), position = "dodge")
#' # You need to explicitly specify the height for dodging
#' p + geom_linerange(aes(ymin = y-1, ymax = y+1),
#'   position = position_dodge(width = 0.9))
#'
#' # Similarly with error bars:
#' p + geom_errorbar(aes(ymin = y-1, ymax = y+1), width = 0.2,
#'   position = "dodge")
#' p + geom_errorbar(aes(ymin = y-1, ymax = y+1, height = 0.2),
#'   position = position_dodge(width = 0.90))
#'
position_dodgev <- function(height = NULL) {
    ggproto(NULL, PositionDodgeV, height = height)
}



PositionDodgeV <- ggproto("PositionDodgeV", Position,
                          required_aes = "y",
                          height = NULL,
                          setup_params = function(self, data) {
                              if (is.null(data$ymin) && is.null(data$ymax) && is.null(self$height)) {
                                  warning("height not defined. Set with `position_dodgev(height = ?)`",
                                          call. = FALSE)
                              }
                              list(height = self$height)
                          },
                          
                          compute_panel = function(data, params, scales) {
                              collidev(data, params$height, "position_dodgev", pos_dodgev, check.height = FALSE)
                          }
)
```


Third, plot the model predictions for effect size (Hedges' g) for male, female and both sexes under both stressed and unstressed condition and faceted for each taxon. 

> I would liker to know how to join a table with mean, and CI values to the forest plots I am generating.

```{r, fig.height= 7, fig.width=10}
pd <- position_dodgev(height = .7)
predictions.complete %>% ggplot(aes(x = pred, y= Environment, colour = Sex)) + 
  geom_vline(xintercept = 0, linetype = 2, colour = "grey70") + 
  geom_errorbarh(aes(xmin = predictions.complete$ci.lb, xmax = predictions.complete$ci.ub), height = 0, position = pd) +
  geom_point(position = pd, size=2) + 
  facet_grid(Taxon ~.)+
  ylab("Environment")+
  xlab("Model Prediction (Hedges g)")+
  xlim(-2, 2)+
  ggtitle('Effects of Sex, Stress and Outcome Class \non Population Fitness for Each Taxon')
```

This model indicates quite a bit of heterogeneity between taxon. More so that that confidence limits increase, rather than radically changing direction of effect. However we can se from this that under stressed environments, females twnd to 

____________

####Does sexual selection benefit populations in stressed environments more than unstressed environments? **AND** Do the benefits of sexual selection accrue more for female fitness components?

Here we run a three level model where the outcome is nested within a study (Study.ID). Other potential random effects include Species and Group.ID. However the estimate (variance from random effect) of these two other potential random effects tended towards zero and were dropped from the model. Additionally the model could be run with just Study.ID, but from our exploration of heterogeneity (below) we see that there is sufficient correlation (but not ICC = 1) between outcomes to include it as a random effect within the model. 

```{r}
#run model without taxon: 3 level model with outcomes within a study
model.complete2 <- rma.mv(g, var.g, 
                          mods = ~ 1 + Sex + Environment + Sex:Environment, 
                          random = ~ 1 | Study.ID/Outcome, 
                          method = "REML", 
                          data = restricted.data)
summary(model.complete2)
```


```{r, fig.height= 7, fig.width=10}

#Generate predictions without taxon utilising the previously described function

get.predictions.complete2 <- function(newdata){
  B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
  if(newdata[1] == "B") B<-1 
  if(newdata[1] == "F") F<-1 
  if(newdata[2] == "Stressed") Unstressed<-1
  if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
  if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1

  predict(model.complete2, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.complete2 <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
                           Environment = c("Unstressed", "Stressed")))
predictions.complete2 <- cbind(predictions.complete2, do.call("rbind", apply(predictions.complete2, 1, get.predictions.complete2))) %>%
  select(Sex, Environment, pred, se, ci.lb, ci.ub) 
for(i in 3:6) predictions.complete2[,i] <- unlist(predictions.complete2[,i])

#And plot the results

pd <- position_dodgev(height = .5)
predictions.complete2 %>% ggplot(aes(x = pred, y= Environment, colour = Sex)) + 
  geom_vline(xintercept = 0, linetype = 2, colour = "grey70") + 
  geom_errorbarh(aes(xmin = predictions.complete2$ci.lb, xmax = predictions.complete2$ci.ub), height = 0, position = pd) +
  geom_point(position = pd) + 
  ylab("Environment")+
  xlab("Effect Size (Hedges g)")+
  xlim(-.75, .75)+
  ggtitle('Effects of Sex, Stress \non Population Fitness')
```

We see that female fitness in stressed environments is greater than the other measurements. For outcomes that were measured for both female and males we see a greater uncertainty in the estimate. It is not obviously clear why this is. The 'both' outcomes are restricted to extinction rate, offspring viability, mutant frequency and reproductive success. However, the shift from 'both' being ns in unstressed to significant in stressed may reflect the dampening of the negative correlations (sexual antagonism).

```{r}
table(restricted.data$Outcome, restricted.data$Sex)
```



__________________________________
###Estimating Heterogeneity using _I^2^_ and exploring correlations


Let's obtain a _I^2^_ statistic using the formulas presented here: http://www.metafor-project.org/doku.php/tips:i2_multilevel_multivariate


There are different methods to obtain estimates of _I^2^_, they should be pretty similar though. Here we obtain an overall value of _I^2^_ that is weighted based on variance where estimates of heterogeneity are sourced from sigma^2^ of the respective models. 
```{r}
#There are two estimates of heterogeneity: A between cluster (between studies) and a within-cluster (outcomes within a study)

#This is for the model with outcome within study
W <- diag(1/restricted.data$var.g)
X <- model.matrix(model.complete2)
P <- W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
100 * sum(model.complete2$sigma2) / (sum(model.complete2$sigma2) + (model.complete2$k-model.complete2$p)/sum(diag(P)))
```
This is a reasonably high _I^2^_ value but is relatively common in Ecology and Evolution (Nakagawa 2017).

To investigate the sources of heterogeneity we can obtain a breakdown of the heterogeneity for the three level model.
```{r}
100 * model.complete2$sigma2 / (sum(model.complete2$sigma2) + (model.complete2$k-model.complete2$p)/sum(diag(P)))
```

This indicates that 24.3 % of total heterogeneity is due to the between study heterogeneity and 68.275 % for within study heterogeneity between different outcomes. With the remaining 7.5 % due to sampling variance. Interestingly this might indicate that _I^2^_ would be largely reduced for a model restricted to a single outcome... Let's test this with our most common outcome...Reproductive Success

```{r}
#Reproductive Success Restriction 
restricted.dataRS <- restricted.data %>% filter(restricted.data$Outcome == "Reproductive Success")

#Run reproductive success only model 
model.completeRS <- rma.mv(g, var.g, mods = ~ 1 + Sex + Environment + Sex:Environment, random = ~ 1 | Study.ID, method = "REML", data = restricted.dataRS)


#Run estimate of heterogeneity
W2 <- diag(1/restricted.dataRS$var.g)
X2 <- model.matrix(model.completeRS)
P2 <- W2 - W2 %*% X2 %*% solve(t(X2) %*% W2 %*% X2) %*% t(X2) %*% W2
100 * sum(model.completeRS$sigma2) / (sum(model.completeRS$sigma2) + (model.completeRS$k-model.completeRS$p)/sum(diag(P2)))
```

So if we look at an individual outcomes such as reproductive success our I^2 is lower (79.53 %). Which is still high as it comes from 39 studies but lower than others. If we wanted to run models independently we could do it for those with a large enough sample size (k>10).

Furthermore, we can obtain estimates of the intra-class correlation (ICC) within a study via: 
```{r}
round(model.complete2$sigma2[1] / sum(model.complete2$sigma2), 3)
```

This means that within a study, between different outcomes, there is a correlation of 26.2 % (low-medium). This justifies including outcome as a level, as without it we would be assuming ICC = 1. We can also gain an estimate of the total heterogeniety, as the sum of the sigma componenets: 
```{r}
round(sum(model.complete2$sigma2), 3)
```

____________



#### Multilevel model using metafors alternative random effect structure 

> This inner, outer factor stuff from the metafor package is a bit strange. There is a description in ``?rma.mv()`` but still unsure how it differs from the above model. It seems that it is useful to breakdown variance-covariance matrix but unsure how that would benefit our analysis.

```{r}
#Just to check, how about with outcome as the inner factor 
model.complete2.2 <- rma.mv(g, var.g, mods = ~ 1 + Sex + Environment + Sex:Environment, random = ~ factor(Outcome) | Study.ID, method = "REML", data = restricted.data)

summary(model.complete2.2)

#Now with a slightly different structure (HCS)

model.complete2.3 <- rma.mv(g, var.g, mods = ~ 1 + Sex + Environment + Sex:Environment, random = ~ Outcome | Study.ID, struct = "HCS", method = "REML", data = restricted.data)

summary(model.complete2.3)
```

```{r, fig.height= 7, fig.width=10}
#Generate predictions without taxon

get.predictions.complete2 <- function(newdata){
  B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
  if(newdata[1] == "B") B<-1 
  if(newdata[1] == "F") F<-1 
  if(newdata[2] == "Stressed") Unstressed<-1
  if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
  if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1

  predict(model.complete2.2, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.complete2 <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
                           Environment = c("Unstressed", "Stressed")))
predictions.complete2 <- cbind(predictions.complete2, do.call("rbind", apply(predictions.complete2, 1, get.predictions.complete2))) %>%
  select(Sex, Environment, pred, se, ci.lb, ci.ub) 
for(i in 3:6) predictions.complete2[,i] <- unlist(predictions.complete2[,i])

#And plot the results

pd <- position_dodgev(height = .3)
predictions.complete2 %>% ggplot(aes(x = pred, y= Environment, colour = Sex)) + 
  geom_vline(xintercept = 0, linetype = 2, colour = "grey70") + 
  geom_errorbarh(aes(xmin = predictions.complete2$ci.lb, xmax = predictions.complete2$ci.ub), height = 0, position = pd) +
  geom_point(position = pd) + 
  ylab("Environment")+
  xlab("Effect Size (Hedges g)")+
  xlim(-.75, .75)+
  ggtitle('Effects of Sex, Stress \non Population Fitness (OUTCOME = INNER FACTOR)')
```




____________________

## Meta-Analysis on Variance

This meta-analysis on variation utilises previously described and utilised methods devoleped (Nakagawa et al. 2015; Senior et al. 2016). Our goal is to determine whether the phenotypic variance in fitness related traits is impacted by sexual selection. We would assume that if selection is occuring not only would the trait mean shift in a certain direction but the variance associated with those changes to the mean would also decrease. In this case we use an effect size statistic known as the natural log of the coefficient of variation ratio (lnCVR)


First, we setup our calculation by creating a a restricted dataset with only unabmiguous fitness outcomes and running the functions developed by Nakagawa et al. 2015: 

```{r}
#Setup restricted data
prelim.data2 <- (prelim.data %>% filter(Outcome.Class != "Ambiguous"))

#Run function for lnCVR and associated variance of lnCVR

#for lnCVR


Calc.lnCVR<-function(CMean, CSD, CN, EMean, ESD, EN){
	
	ES<-log(ESD) - log(EMean) + 1 / (2*(EN - 1)) - (log(CSD) - log(CMean) + 1 / (2*(CN - 1)))
	
	return(ES)
	
}

#for variance of lnCVR

Calc.var.lnCVR<-function(CMean, CSD, CN, EMean, ESD, EN, Equal.E.C.Corr=T){
	
	if(Equal.E.C.Corr==T){
	
		mvcorr<-cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate
	
		S2<- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * mvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * mvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
	
	}
	else{
		
		Cmvcorr<-cor.test(log(CMean), log(CSD))$estimate
		Emvcorr<-cor.test(log(EMean), (ESD))$estimate
	
		S2<- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * Cmvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * Emvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))		
		
		
	}
	return(S2)
	
}

```

Secondly, we utilise those formulas to obtain lnCVR and var.CVR for all applicable effect sizes. Noting that not all of the dataset has means, SD and n; some were calculated from summary statistics and are not able to have lnCVR calculated. Once we obtain these lnCVR estimates we can run subsetted models that use as the response variable: 


Now utilise function with existing data frame 
```{r, fig.height= 7, fig.width=10}
#foe lnCVR
prelim.data2$lnCVr <- Calc.lnCVR(prelim.data2$mean.low, prelim.data2$sd.low, prelim.data2$n.low, prelim.data2$mean.high, prelim.data2$sd.high, prelim.data2$n.high)

#for variance in lnCVR
prelim.data2$var.lnCVr <- Calc.var.lnCVR(prelim.data2$mean.low, prelim.data2$sd.low, prelim.data2$n.low, prelim.data2$mean.high, prelim.data2$sd.high, prelim.data2$n.high, Equal.E.C.Corr=F)


#Run simple models subsetted for each environment/sex (this is perhaps a clunky way so we also use predictions shown below)

# For stressed environment and females
varSF <- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~ 1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Stressed" & Sex == "F"))

# For stressed environment and females
varSM <- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~ 1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Stressed" & Sex == "M"))

# For stressed environment and females
varSB <- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~ 1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Stressed" & Sex == "B"))

#For Benign environment and females
varUF<- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Unstressed" & Sex == "F"))

#For Benign environment and males
varUM<- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Unstressed" & Sex == "M"))

#For Benign environment and both
varUB <- rma.mv(lnCVr, var.lnCVr, data = prelim.data2, mods = ~1, random = ~ 1 | Study.ID/Outcome, subset = (Environment == "Unstressed" & Sex == "B"))

#Create dataframe of estimates and confidence intervals
lnCVR <- c(varSF$b, varSM$b, varSB$b, varUF$b, varUM$b, varUB$b)
l.ci <- c(varSF$ci.lb, varSM$ci.lb, varSB$ci.lb, varUF$ci.lb, varUM$ci.lb, varUB$ci.lb)
u.ci <- c(varSF$ci.ub, varSM$ci.ub, varSB$ci.ub, varUF$ci.ub, varUM$ci.ub, varUB$ci.ub)
Environment <- c("Stressed", "Stressed", "Stressed", "Unstressed", "Unstressed", "Unstressed")
Sex <- c("Female", "Male", "Both", "Female", "Male", "Both")
k <- c(varSF$k, varSM$k, varSB$k, varUF$k, varUM$k, varUB$k)

var.data <- data.frame(lnCVR, l.ci, u.ci, Environment, Sex, k)

#Releveling the factors to make sure it aligns with other formatted graphs
var.data$Environment <- var.data$Environment %>% factor %>% relevel(ref="Unstressed")
var.data$Sex <- var.data$Sex %>% factor %>% relevel(ref="Male")

#Plot subseted model estimates

var.data %>% ggplot(aes(x=lnCVR, y = Environment, colour = Sex))+ 
  geom_vline(xintercept = 0, linetype = 2, colour = "grey70") + 
  geom_errorbarh(aes(xmin = l.ci, xmax = u.ci), height = 0, position = pd) +
  geom_point(position = pd) + 
  ylab("Environment")+
  xlab("lnCVR")+
  xlim(-.75, .75)+
  ggtitle('Meta-Analysis of Variance (Using Subsetting)')

```


Here we see that Stressed environments tend to reduce phenotypic variance (if anything). However, perhaps a better way to conduct this analysis is not through subsetting but through utilising model predictions as we did with Hedges' g previously. This can be done be utilising the same predict function but for lnCVR and var.lnCVR. 

Multilevel-model using lnCVR:

```{r}
#Now try with multilevel model 
variance.model <- rma.mv(lnCVr, var.lnCVr, mods = ~ 1 + Sex + Environment + Sex:Environment, random = ~ 1 | Study.ID/Outcome, method = "REML", data = prelim.data2 %>% filter(Environment != "Not Stated"))
summary(variance.model)
```

Plotted predictions of lnCVR for various moderators: 

```{r, fig.height= 7, fig.width=10}

#Generate predictions
get.predictions.variance <- function(newdata){
  F<-0; M<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
  if(newdata[1] == "F") F<-1 
  if(newdata[1] == "M") M<-1 
  if(newdata[2] == "Stressed") Unstressed<-1
  if(newdata[1] == "F" & newdata[2] == "Stressed") interaction1<-1
  if(newdata[1] == "M" & newdata[2] == "Stressed") interaction2<-1

  predict(variance.model, newmods=c(F, M, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.variance <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
                           Environment = c("Unstressed", "Stressed")))
predictions.variance <- cbind(predictions.variance, do.call("rbind", apply(predictions.variance, 1, get.predictions.variance))) %>%
  select(Sex, Environment, pred, se, ci.lb, ci.ub) 
for(i in 3:6) predictions.variance[,i] <- unlist(predictions.variance[,i])

#And plot the results

pd <- position_dodgev(height = .3)
predictions.variance %>% ggplot(aes(x = pred, y= Environment, colour = Sex)) + 
  geom_vline(xintercept = 0, linetype = 2, colour = "grey70") + 
  geom_errorbarh(aes(xmin = predictions.variance$ci.lb, xmax = predictions.variance$ci.ub), height = 0, position = pd) +
  geom_point(position = pd) + 
  ylab("Environment")+
  xlab("lnCVR")+
  xlim(-1.2, 1.2)+
  ggtitle('Meta-Analysis of Variance (Using Model Predictions)')
```

From these predictions we see that environmental stress has a large impact on phenotypic variance, whereby phenotypic variance is lower for females than for males and under stressful conditions the phenotypic variance of females decreases. Indicating there is a narrowing of phenotypic variance under selection. 

____________________
##Bias

###Funnel plots indicate potential but small amounts of publication bias

Checking for biases with a funnell plot. Note that the trim and fill method does not work with rma.mv objects. However we can perform Eggers test using the ``ragtest()`` function. This tests for asymmetry via assessing relationships between effect size and a specified predictor. See ``?ragtest()`` for more information. Also the eggers test does not work for rma.mv objects. 
```{r}
trim.ambig <- trimfill.rma.uni(model.Ambiguous)
funnel.rma(trim.ambig)
regtest(model.Ambiguous, predictor = "vi")
funnel.rma(model.Indirect)
regtest(model.Indirect, predictor = "vi")
funnel.rma(model.Direct)
regtest(model.Direct, predictor = "vi")
funnel.rma(model.complete2, type="rstandard", yaxis = "sei")
```

>Now use ggplot for funnel plot. This is pretty clunky and unlike the ``funnel.rma`` it does not use residuals but raw effect sizes, we could of course add residuals, so it depends on how much customization you think these plots should have. The outline taken from: https://sakaluk.wordpress.com/2016/02/16/7-make-it-pretty-plots-for-meta-analysis/

```{r, fig.height= 7, fig.width=10}
#Funnel plot for indirect model

#Make restricted data for indirect

prelim.data.Indirect <- prelim.data %>% filter(Outcome.Class == "Indirect")

#read in apatheme
#My APA-format theme
apatheme=theme_bw()+
  theme(panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        panel.border=element_blank(),
        axis.line=element_line(),
        text=element_text(family='Times'),
        legend.position='none')

#Store the meta-analytic estimate and its standard error from whatever model you run (substitute your own values)
estimate = model.Indirect$b
se = model.Indirect$se
 
#Store a vector of values that spans the range from 0
#to the max value of impression (standard error) in your dataset.
#Make the increment (the final value) small enough (I choose 0.001)
#to ensure your whole range of data is captured
se.seq=seq(0, max(sqrt(prelim.data.Indirect$var.g)), 0.001)
 
#Now, compute vectors of the lower-limit and upper limit values for
#the 95% CI region, using the range of SE that you generated in the previous step, and the stored value of your meta-analytic estimate.
ll95 = estimate-(1.96*se.seq)
ul95 = estimate+(1.96*se.seq)
 
#You can do this for a 99% CI region too
ll99 = estimate-(3.29*se.seq)
ul99 = estimate+(3.29*se.seq)
 
#And finally, do the same thing except now calculating the confidence interval
#for your meta-analytic estimate based on the stored value of its standard error
meanll95 = estimate-(1.96*se)
meanul95 = estimate+(1.96*se)
 
#Now, smash all of those calculated values into one data frame (called 'dfCI').
#You might get a warning about '...row names were found from a short variable...'
#You can ignore it.
dfCI = data.frame(ll95, ul95, ll99, ul99, se.seq, estimate, meanll95, meanul95)
 
#Now we can actually make the funnel plot.
#Using your original data-frame, map standard error to your x-axis (for now) and Zr to your y-axis
fp = ggplot(prelim.data.Indirect, aes_string(x = sqrt(prelim.data.Indirect$var.g), y =prelim.data.Indirect$g)) +
#Add your data-points to the scatterplot
  geom_point(shape = 1) +
#Give the x- and y- axes informative labels
  xlab('Standard Error') + ylab('g')+
#Now using the 'dfCI' data-frame we created, plot dotted lines corresponding
#to the lower and upper limits of your 95% CI region,
#And dashed lines corresponding to your 99% CI region
  geom_line(aes(x = se.seq, y = ll95), linetype = 'dotted', data = dfCI) +
  geom_line(aes(x = se.seq, y = ul95), linetype = 'dotted', data = dfCI) +
  geom_line(aes(x = se.seq, y = ll99), linetype = 'dashed', data = dfCI) +
  geom_line(aes(x = se.seq, y = ul99), linetype = 'dashed', data = dfCI) +
#Now plot dotted lines corresponding to the 95% CI of your meta-analytic estimate
   geom_segment(aes(x = min(se.seq), y = meanll95, xend = max(se.seq), yend = meanll95), linetype='dotted', data=dfCI) +
  geom_segment(aes(x = min(se.seq), y = meanul95, xend = max(se.seq), yend = meanul95), linetype='dotted', data=dfCI) +
#Reverse the x-axis ordering (se) so that the tip of the funnel will appear
#at the top of the figure once we swap the x- and y-axes...
  scale_x_reverse()+
#Specify the range and interval for the tick-marks of the y-axis (Zr);
#Choose values that work for you based on your data
  scale_y_continuous(breaks=seq(-1.25,2,0.25))+
#And now we flip the axes so that SE is on y- and Zr is on x-
  coord_flip()+
#Finally, apply my APA-format theme (see code at end of post).
#You could, alternatively, specify theme_bw() instead.
  apatheme
 
#Call the pretty funnel plot
fp
```

###Other tests of publication bias suggest there is little evidence for publication bias

####Journal Impact Factor

If we see a positive trend with effect size and Journal Impact Factor it may represent publication bias whereby significant (positive) results are published more readily and in more circulated journals. Our journal impact factor dataset is not evenly distributed as several publications in Nature (JIF ~ 40) are much larger than the next highest JIF (~11). 

```{r}

```


```{r, fig.width=10, fig.height=8}
prelim.data %>% ggplot(aes(x=JIF, y=(g)))+
  geom_jitter(color='darkgreen', alpha=.3, aes(size = (1/(var.g))))+
  geom_hline(yintercept=0, linetype = 'dotted')+
  geom_smooth(method='lm', color='black')+
  labs(size = 'weight')
```

> I think it may also be interesting to plot the maximum effect size for each study and then plot the results as many studies measure a suite of traits.


####Time-lag Bias


We can also look at the time-lag bias, which suggests effect size decreases over time. Again, because one publication from 1980 is well before the next publication in the late 1990s we see a very uneven distribution.

```{r, fig.width=7.50, fig.height=6}
prelim.data %>% ggplot(aes(x=Year, y=g))+
  geom_jitter(color='orange', alpha=.3, aes(size = (1/(var.g))))+
  geom_hline(yintercept=0, linetype = 'dotted')+
  geom_smooth(method='lm', color='black')+
  labs(size = 'weight')
```

####Blinding

From these graphs, we see a small trend for larger effect sizes to be published in higher impact journals as well as for effect size to decrease over successive years. Additionally to publication bias, other forms of bias may exist within studies. We initially collected data on whether studies were blind or not. Although not enough studies were blind for us to include it as a fixed effect within the model we can test whether blinding affects the raw effect size: 

```{r, fig.width=10, fig.height=8}
df.forest.model %>% ggplot(aes(x=Blinding, y=g))+
  geom_boxplot()+
  geom_jitter(aes(color=Blinding))+
  geom_hline(yintercept=0, linetype = 'dotted')
```

####Sample Size


We also collected sample sizes for each of the effect sizes calculated. Because we are dealing withj different taxa some studies are not suited to have sample sizes in the 1000's. We can simply inspect the sample size ande effect sizes through the following plot:
```{r, fig.width=5, fig.height=10}
prelim.data %>% ggplot(aes(x=(n), y = g))+
  geom_point()+
  xlim(0,2100)+
  facet_grid(Taxon~., scales='free')+
  ylim(-3.5,3.5)+
  geom_hline(yintercept=0, linetype="dashed")
```

(Promislow 1998 has one sample size of >10,000) and is not shown here. From these plots we can see that with increased sample size the effect sizes are closer to zero. Thistrend should be taken into account as meta-analytic models are wighted by 1/variance. 


####Generations

We recorded the number of generations of experimental exolution each study used. The number of generations proved a negligable predictor in the meta-analytic models and can be seen here: 

```{r, fig.width=8, fig.height=7}
prelim.data %>% ggplot(aes(x=Generations, y=g, color=Taxon))+
  geom_point()+
  ylim(-3.5,3.5)+
  geom_hline(yintercept=0, linetype="dashed")
```


